


"""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 			__lowerCamelCase     (    a__     ,    a__     ,    a__ ):

     '''simple docstring'''
     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 = 50257			, __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			, )				->							List[str]:
          super().__init__()

          _a					= 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.'							)

          _a					= prefix_inner_dim
          _a					= prefix_hidden_dim

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

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


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, )				->							Union[str, Any]:
          _a					= self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_							)
          _a					= self.encode_prefix(SCREAMING_SNAKE_CASE_							)
          _a					= self.decode_prefix(SCREAMING_SNAKE_CASE_							)
          _a					= torch.cat((prefix_embeds, embedding_text)			, dim=1							)

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


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase							)				->							torch.Tensor:
          return torch.zeros(SCREAMING_SNAKE_CASE_			, self.prefix_length			, dtype=torch.intaa			, device=SCREAMING_SNAKE_CASE_							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							List[str]:
          return self.encode_prefix(SCREAMING_SNAKE_CASE_							)


     @torch.no_grad()
     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Optional[int]:
          _a					= torch.split(SCREAMING_SNAKE_CASE_			, 1			, dim=0							)
          _a					= []
          _a					= []
          for feature in features:
               _a					= self.decode_prefix(feature.to(SCREAMING_SNAKE_CASE_							)							)  # back to the clip feature
               # Only support beam search for now
               _a		,					_a					= self.generate_beam(
                   input_embeds=SCREAMING_SNAKE_CASE_			, device=SCREAMING_SNAKE_CASE_			, eos_token_id=SCREAMING_SNAKE_CASE_							)
               generated_tokens.append(output_tokens[0]							)
               generated_seq_lengths.append(seq_lengths[0]							)
          _a					= torch.stack(SCREAMING_SNAKE_CASE_							)
          _a					= torch.stack(SCREAMING_SNAKE_CASE_							)
          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			, )				->							List[str]:
          _a					= eos_token_id
          _a					= None
          _a					= None
          _a					= torch.ones(SCREAMING_SNAKE_CASE_			, device=SCREAMING_SNAKE_CASE_			, dtype=torch.int							)
          _a					= torch.zeros(SCREAMING_SNAKE_CASE_			, device=SCREAMING_SNAKE_CASE_			, dtype=torch.bool							)

          if input_embeds is not None:
               _a					= input_embeds
          else:
               _a					= self.transformer.transformer.wte(SCREAMING_SNAKE_CASE_							)

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

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

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

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

import argparse
import os
import re


__a  			=					'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a  			=					re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__a  			=					re.compile(r'\s*\(\s*"(\S[^"]+)"')




def 				a						(							snake_case__:					str						,   snake_case__:					bool = False					):


				'''simple docstring'''
				with open(snake_case__						,   '''r'''						,   encoding='''utf-8'''					) as f:
								lowercase_       =					f.read()

				lowercase_       =					content.split('''\n'''					)
				lowercase_       =					[]
				lowercase_       =					0
				while line_idx < len(snake_case__					):
								if _re_intro_mapping.search(lines[line_idx]					) is not None:
												lowercase_       =					len(re.search(r'''^(\s*)\S'''						,   lines[line_idx]					).groups()[0]					) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(''' ''' * indent + '''('''					):
																new_lines.append(lines[line_idx]					)
																line_idx += 1

												lowercase_       =					[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				lowercase_       =					line_idx
																				while not lines[line_idx].startswith(''' ''' * indent + ''')'''					):
																								line_idx += 1
																				blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]					)					)
																else:
																				blocks.append(lines[line_idx]					)
																line_idx += 1

												# Sort blocks by their identifiers
												lowercase_       =					sorted(snake_case__						,   key=lambda snake_case__					: _re_identifier.search(snake_case__					).groups()[0]					)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]					)
												line_idx += 1

				if overwrite:
								with open(snake_case__						,   '''w'''						,   encoding='''utf-8'''					) as f:
												f.write('''\n'''.join(snake_case__					)					)
				elif "\n".join(snake_case__					) != content:
								return True




def 				a						(							snake_case__:					bool = False					):


				'''simple docstring'''
				lowercase_       =					[os.path.join(snake_case__						,   snake_case__					) for f in os.listdir(snake_case__					) if f.endswith('''.py'''					)]
				lowercase_       =					[sort_auto_mapping(snake_case__						,   overwrite=snake_case__					) for fname in fnames]

				if not overwrite and any(snake_case__					):
								lowercase_       =					[f for f, d in zip(snake_case__						,   snake_case__					) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {', '.join(snake_case__					)}. Run `make style` to fix'''
								    ''' this.'''					)


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						__a  			=					parser.parse_args()

						sort_all_auto_mappings(not args.check_only)




