Project: 059_Retrieval_Head_Mechanistically_Explains_Long-Conte | Function: retrieval_calculate (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Core algorithm that calculates retrieval scores for attention heads by detecting copy-paste operations. Implements the fundamental retrieval head detection mechanism that identifies which attention heads are responsible for retrieving information from input contexts.

**KEY TERMS:** {
  "retrieval_score": "retrieval score for head h",
  "needle_start": "needle sentence position iq",
  "needle_end": "end of needle sentence",
  "topk": "arg max(a) operation",
  "head_counter": "accumulation of retrieval scores gh"
}

**CONTEXT CODE:**                 ).eval()
        elif "Mixtral" in self.model_version:
            self.model_to_test = MixtralForCausalLM.from_pretrained(
                    model_name,torch_dtype="auto",device_map='auto',use_flash_attention_2="flash_attention_2",trust_remote_code=True,
                ).eval()
        elif "Mistral" in self.model_version:
            self.model_to_test = MistralForCausalLM.from_pretrained(
                    model_name,torch_dtype="auto",device_map='auto',use_flash_attention_2="flash_attention_2",trust_remote_code=True,
                ).eval()
        elif "Phi3" in self.model_version:
            self.model_to_test = Phi3ForCausalLM.from_pretrained(
                    model_name,torch_dtype="auto",device_map='auto',use_flash_attention_2="flash_attention_2",trust_remote_code=True,
                ).eval()
        else:
            self.model_to_test = LlamaForCausalLM.from_pretrained(model_name,
                use_flash_attention_2="flash_attention_2", torch_dtype=torch.bfloat16,device_map='auto').eval()
            
        if 'llama-2-7b-80k' in self.model_version:
            scaling_factor = 10
            reset_rope(self.model_to_test, model_max_train_len=81920, scaling_factor=scaling_factor)
            
        if "CUDA_VISIBLE_DEVICES" in os.environ:
            self.multi_gpus = len(os.environ["CUDA_VISIBLE_DEVICES"])>1
        else:
            self.multi_gpus = True
            
        self.model_to_test_description = model_name
        self.evaluation_model = None
        self.debug='debug'
        model_name = model_name.split('/')[-1]

    def logistic(self, x, L=100, x0=50, k=.1):
        if x == 0:
            return 0
        if x == 100:
            return 100
        return np.round(L / (1 + np.exp(-k * (x - x0))), 3)
    
    def bound_evaluate_and_log(self, *args):
        self.evaluate_and_log(*args)

    def run_test(self, args):
        # Run through each iteration of context_lengths and depths
        tasks = []
         
        for context_length in self.context_lengths:
            if context_length < args.s_len or context_length > args.e_len: continue
            for depth_percent in self.document_depth_percents:
                task = self.bound_evaluate_and_log(context_length, depth_percent)

    def retrieval_calculate(self, attention_maxtrix,retrieval_score, inp, step_token,topk=1):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    def retrieval_head_accumulate(self, retrieval_score):
        for layer_idx in range(self.layer_num):
            for head_idx in range(self.head_num):
                self.head_counter[f"{layer_idx}-{head_idx}"].append(retrieval_score[layer_idx][head_idx][0])

    def decode(self, q_outputs, inp, decode_len, block_list=None):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    def find_needle_idx(self, needle):
        needle_ids = self.enc(needle, add_special_tokens=False)["input_ids"]
        print( self.enc.decode(needle_ids, skip_special_tokens=False))
        span_len = len(needle_ids)
        for i in range(len(self.prompt_ids)):            
            token_span = self.prompt_ids[i : i + span_len]
            span_ids = set(token_span.tolist())
            overlap = float(len(span_ids.intersection(set(needle_ids)))) / len(set(needle_ids))
            if(overlap > 0.9):
                return i, i + span_len
        return -1, -1

    def evaluate_and_log(self, context_length, depth_percent):
        # Checks to see if you've already checked a length/percent/version.
        # This helps if the program stop running and you want to restart later
        # Go generate the required length context and place your needle statement in
        context = self.generate_context(context_length, depth_percent)
        question = f"Based on the content of the book, Question: {self.retrieval_question}\nAnswer:"
        '''
        if self.model_version=="Qwen1.5-14B-Chat":
            context = "
system\nYou are a helpful assistant
\n
user\n" + context input_context = "f{context}\nquestion
\n
assistant\n
            question += '
\n
assistant\n'
            input_ids = self.enc(input_context , return_tensors="pt")['input_ids']
        '''
        if self.model_version in ["Mistral-7B-Instruct-v0.2", "Qwen1.5-14B-Chat"]:
            prompt = [
            {"role": "user", "content": f"<book>{context}</book>\nBased on the content of the book, Question: {self.retrieval_question}\nAnswer:"},
            ]
            input_ids = self.enc.apply_chat_template(conversation=prompt, tokenize=True,  add_generation_prompt=True, return_tensors='pt')
        else:
            input_context = context + question
            input_ids = self.enc(input_context , return_tensors="pt")['input_ids']
        
        # Prepare your message to send to the model you're going to evaluate
        test_start_time = time.time()
        self.prompt_ids = input_ids[0, :]
        if not self.multi_gpus:
            input_ids = input_ids.to(self.model_to_test.device)
        self.needle_start, self.needle_end = self.find_needle_idx(self.real_needle)
        with torch.no_grad():
            q_outputs = self.model_to_test(input_ids=input_ids[:,:-1], use_cache=True, return_dict=True)
            output, retrieval_score  = self.decode(q_outputs, input_ids[:,-1], 50)
            response = self.enc.decode(output,skip_special_tokens=True).strip()

        test_end_time = time.time()
        test_elapsed_time = test_end_time - test_start_time
        
        score = scorer.score(self.real_needle, response)['rouge1'].recall*100
        ## if recall > 50, we determine this retrieval succeed and update the retrieval score
        if score > 50:
            self.retrieval_head_accumulate(retrieval_score)
            head_score = [(i[0], np.mean(i[1])) for i in self.head_counter.items()]
            head_score = sorted(head_score, key=lambda x:x[1], reverse=True)
            print([[i[0]] for i in head_score][:20])

        results = {
            'model' : self.model_to_test_description,
            'context_length' : int(context_length),
            'depth_percent' : float(depth_percent),
            'version' : self.results_version,
            'needle' : self.needle,
            'model_response' : response,
            'score' : score,
            'test_duration_seconds' : test_elapsed_time,
            'test_timestamp_utc' : datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S%z')
        }

        self.testing_results.append(results)

        if self.print_ongoing_status:
            print (f"-- Test Summary -- ")
            print (f"Duration: {test_elapsed_time:.1f} seconds")
            print (f"Context: {context_length} tokens")
            print (f"Depth: {depth_percent}%")
            print (f"Score: {score}")
            print (f"Response: {response}\n")

        context_file_location = f'{self.model_version.replace(".", "_")}_len_{context_length}_depth_{int(depth_percent*100)}'

        if self.save_contexts:
            results['file_name'] : context_file_location

            # Save the context to file for retesting
            if not os.path.exists('contexts'):
                os.makedirs('contexts')

            if not os.path.exists(f'contexts/{self.model_version}'):
                os.makedirs(f'contexts/{self.model_version}')



Complete the function implementation replacing "raise NotImplementedError".