



'''simple docstring'''



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__ ):
     a      :     Optional[int]        =							[R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]



     @register_to_config
     def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = 5_0257			,				UpperCamelCase = 1024			,				UpperCamelCase = 768			,				UpperCamelCase = 12			,				UpperCamelCase = 12			,				UpperCamelCase = None			,				UpperCamelCase = "gelu_new"			,				UpperCamelCase = 0.1			,				UpperCamelCase = 0.1			,				UpperCamelCase = 0.1			,				UpperCamelCase = 1E-5			,				UpperCamelCase = 0.02			,				UpperCamelCase = True			,				UpperCamelCase = True			,				UpperCamelCase = False			,				UpperCamelCase = False			,				)     ->			Tuple:
      super().__init__()

      __lowerCAmelCase												=  prefix_length

      if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
       raise ValueError(
           F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
           F''' `n_embd`: {n_embd} are not equal.'''     )

      __lowerCAmelCase												=  prefix_inner_dim
      __lowerCAmelCase												=  prefix_hidden_dim

      __lowerCAmelCase												=  (
          nn.Linear(self.prefix_inner_dim			,				self.prefix_hidden_dim     )
          if self.prefix_hidden_dim is not None
          else nn.Identity()
      )
      __lowerCAmelCase												=  (
          nn.Linear(self.prefix_hidden_dim			,				UpperCamelCase     ) if self.prefix_hidden_dim is not None else nn.Identity()
      )

      __lowerCAmelCase												=  GPTaConfig(
          vocab_size=UpperCamelCase			,				n_positions=UpperCamelCase			,				n_embd=UpperCamelCase			,				n_layer=UpperCamelCase			,				n_head=UpperCamelCase			,				n_inner=UpperCamelCase			,				activation_function=UpperCamelCase			,				resid_pdrop=UpperCamelCase			,				embd_pdrop=UpperCamelCase			,				attn_pdrop=UpperCamelCase			,				layer_norm_epsilon=UpperCamelCase			,				initializer_range=UpperCamelCase			,				scale_attn_weights=UpperCamelCase			,				use_cache=UpperCamelCase			,				scale_attn_by_inverse_layer_idx=UpperCamelCase			,				reorder_and_upcast_attn=UpperCamelCase			,				)
      __lowerCAmelCase												=  GPTaLMHeadModel(UpperCamelCase     )



     def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = None			,				)     ->			str:
      __lowerCAmelCase												=  self.transformer.transformer.wte(UpperCamelCase     )
      __lowerCAmelCase												=  self.encode_prefix(UpperCamelCase     )
      __lowerCAmelCase												=  self.decode_prefix(UpperCamelCase     )
      __lowerCAmelCase												=  torch.cat((prefix_embeds, embedding_text)			,				dim=1     )

      if labels is not None:
       __lowerCAmelCase												=  self.get_dummy_token(input_ids.shape[0]			,				input_ids.device     )
       __lowerCAmelCase												=  torch.cat((dummy_token, input_ids)			,				dim=1     )
      __lowerCAmelCase												=  self.transformer(inputs_embeds=UpperCamelCase			,				labels=UpperCamelCase			,				attention_mask=UpperCamelCase     )
      if self.prefix_hidden_dim is not None:
       return out, hidden
      else:
       return out



     def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			torch.Tensor:
      return torch.zeros(UpperCamelCase			,				self.prefix_length			,				dtype=torch.intaa			,				device=UpperCamelCase     )



     def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[Any]:
      return self.encode_prefix(UpperCamelCase     )



     @torch.no_grad()
     def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			Union[str, Any]:
      __lowerCAmelCase												=  torch.split(UpperCamelCase			,				1			,				dim=0     )
      __lowerCAmelCase												=  []
      __lowerCAmelCase												=  []
      for feature in features:
       __lowerCAmelCase												=  self.decode_prefix(feature.to(UpperCamelCase     )     )  # back to the clip feature
       # Only support beam search for now
       __lowerCAmelCase							,       __lowerCAmelCase												=  self.generate_beam(
           input_embeds=UpperCamelCase			,				device=UpperCamelCase			,				eos_token_id=UpperCamelCase     )
       generated_tokens.append(output_tokens[0]     )
       generated_seq_lengths.append(seq_lengths[0]     )
      __lowerCAmelCase												=  torch.stack(UpperCamelCase     )
      __lowerCAmelCase												=  torch.stack(UpperCamelCase     )
      return generated_tokens, generated_seq_lengths



     @torch.no_grad()
     def        UpperCAmelCase_ (    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase = 5			,				UpperCamelCase = 67			,				UpperCamelCase = 1.0			,				UpperCamelCase = None			,				)     ->			str:
      __lowerCAmelCase												=  eos_token_id
      __lowerCAmelCase												=  None
      __lowerCAmelCase												=  None
      __lowerCAmelCase												=  torch.ones(UpperCamelCase			,				device=UpperCamelCase			,				dtype=torch.int     )
      __lowerCAmelCase												=  torch.zeros(UpperCamelCase			,				device=UpperCamelCase			,				dtype=torch.bool     )

      if input_embeds is not None:
       __lowerCAmelCase												=  input_embeds
      else:
       __lowerCAmelCase												=  self.transformer.transformer.wte(UpperCamelCase     )

      for i in range(UpperCamelCase     ):
       __lowerCAmelCase												=  self.transformer(inputs_embeds=UpperCamelCase     )
       __lowerCAmelCase												=  outputs.logits
       __lowerCAmelCase												=  logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
       __lowerCAmelCase												=  logits.softmax(-1     ).log()

       if scores is None:
        __lowerCAmelCase							,       __lowerCAmelCase												=  logits.topk(UpperCamelCase			,				-1     )
        __lowerCAmelCase												=  generated.expand(UpperCamelCase			,				*generated.shape[1:]     )
        __lowerCAmelCase							,       __lowerCAmelCase												=  next_tokens.permute(1			,				0     ), scores.squeeze(0     )
        if tokens is None:
         __lowerCAmelCase												=  next_tokens
        else:
         __lowerCAmelCase												=  tokens.expand(UpperCamelCase			,				*tokens.shape[1:]     )
         __lowerCAmelCase												=  torch.cat((tokens, next_tokens)			,				dim=1     )
       else:
        __lowerCAmelCase												=  -float(np.inf     )
        __lowerCAmelCase												=  0
        __lowerCAmelCase												=  scores[:, None] + logits
        seq_lengths[~is_stopped] += 1
        __lowerCAmelCase												=  scores_sum / seq_lengths[:, None]
        __lowerCAmelCase							,       __lowerCAmelCase												=  scores_sum_average.view(-1     ).topk(UpperCamelCase			,				-1     )
        __lowerCAmelCase												=  next_tokens // scores_sum.shape[1]
        __lowerCAmelCase												=  seq_lengths[next_tokens_source]
        __lowerCAmelCase												=  next_tokens % scores_sum.shape[1]
        __lowerCAmelCase												=  next_tokens.unsqueeze(1     )
        __lowerCAmelCase												=  tokens[next_tokens_source]
        __lowerCAmelCase												=  torch.cat((tokens, next_tokens)			,				dim=1     )
        __lowerCAmelCase												=  generated[next_tokens_source]
        __lowerCAmelCase												=  scores_sum_average * seq_lengths
        __lowerCAmelCase												=  is_stopped[next_tokens_source]

       __lowerCAmelCase												=  self.transformer.transformer.wte(next_tokens.squeeze()     ).view(generated.shape[0]			,				1			,				-1     )
       __lowerCAmelCase												=  torch.cat((generated, next_token_embed)			,				dim=1     )
       __lowerCAmelCase												=  is_stopped + next_tokens.eq(UpperCamelCase     ).squeeze()
       if is_stopped.all():
        break

      __lowerCAmelCase												=  scores / seq_lengths
      __lowerCAmelCase												=  scores.argsort(descending=UpperCamelCase     )
      # tokens tensors are already padded to max_seq_length
      __lowerCAmelCase												=  [tokens[i] for i in order]
      __lowerCAmelCase												=  torch.stack(UpperCamelCase			,				dim=0     )
      __lowerCAmelCase												=  torch.tensor([seq_lengths[i] for i in order]			,				dtype=seq_lengths.dtype     )
      return output_texts, seq_lengths




'''simple docstring'''



import os
import sys
import unittest


lowerCAmelCase	:		str				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
lowerCAmelCase	:		Tuple				=   os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')






class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_test_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {"BertModelTest": "BertModelTester"}

						__lowerCAmelCase												=  {
						    "BlipModelTest": "BlipModelTester",
						    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
						    "BlipTextModelTest": "BlipTextModelTester",
						    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
						    "BlipVQAModelTest": "BlipVQAModelTester",
						    "BlipVisionModelTest": "BlipVisionModelTester",
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_test_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTest"],
						    "BertForMultipleChoice": ["BertModelTest"],
						    "BertForNextSentencePrediction": ["BertModelTest"],
						    "BertForPreTraining": ["BertModelTest"],
						    "BertForQuestionAnswering": ["BertModelTest"],
						    "BertForSequenceClassification": ["BertModelTest"],
						    "BertForTokenClassification": ["BertModelTest"],
						    "BertLMHeadModel": ["BertModelTest"],
						    "BertModel": ["BertModelTest"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
						    "BlipModel": ["BlipModelTest"],
						    "BlipTextModel": ["BlipTextModelTest"],
						    "BlipVisionModel": ["BlipVisionModelTest"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )
						__lowerCAmelCase												=  get_model_to_tester_mapping(UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "BertForMaskedLM": ["BertModelTester"],
						    "BertForMultipleChoice": ["BertModelTester"],
						    "BertForNextSentencePrediction": ["BertModelTester"],
						    "BertForPreTraining": ["BertModelTester"],
						    "BertForQuestionAnswering": ["BertModelTester"],
						    "BertForSequenceClassification": ["BertModelTester"],
						    "BertForTokenClassification": ["BertModelTester"],
						    "BertLMHeadModel": ["BertModelTester"],
						    "BertModel": ["BertModelTester"],
						}

						__lowerCAmelCase												=  {
						    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
						    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
						    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
						    "BlipModel": ["BlipModelTester"],
						    "BlipTextModel": ["BlipTextModelTester"],
						    "BlipVisionModel": ["BlipVisionModelTester"],
						}

						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )
						self.assertEqual(get_test_info.to_json(UpperCamelCase     )			,				UpperCamelCase     )