




import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case_ : Tuple    		=       "src/transformers"
snake_case_ : Union[str, Any]    		=       "docs/source/en"
snake_case_ : str    		=       "."


def    A						(__A   :    Union[str, Any]   ,				__A   :    str   ,				__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     with open(__snake_case   ,				'''r'''   ,				encoding='''utf-8'''   ,				newline='''\n'''       ) as f:
          UpperCAmelCase_          =							f.readlines()
     # Find the start prompt.
     UpperCAmelCase_          =							0
     while not lines[start_index].startswith(__snake_case       ):
          start_index += 1
     start_index += 1

     UpperCAmelCase_          =							start_index
     while not lines[end_index].startswith(__snake_case       ):
          end_index += 1
     end_index -= 1

     while len(lines[start_index]       ) <= 1:
          start_index += 1
     while len(lines[end_index]       ) <= 1:
          end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
snake_case_ : Dict    		=       "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
snake_case_ : Any    		=       re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
snake_case_ : List[str]    		=       re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case_ : List[Any]    		=       re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
snake_case_ : int    		=       direct_transformers_import(TRANSFORMERS_PATH)


def    A						(__A   :    List[str]       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''   ,				__snake_case       )
     return [m.group(0       ) for m in matches]


def    A						(__A   :    Optional[Any]   ,				__A   :    List[Any]       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							2 if text == '''✅''' or text == '''❌''' else len(__snake_case       )
     UpperCAmelCase_          =							(width - text_length) // 2
     UpperCAmelCase_          =							width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


def    A						()   ->      Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase_          =							{
         name: config_maping_names[code]
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     UpperCAmelCase_          =							{name: config.replace('''Config'''   ,				''''''       ) for name, config in model_name_to_config.items()}

     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     UpperCAmelCase_          =							collections.defaultdict(__snake_case       )
     UpperCAmelCase_          =							collections.defaultdict(__snake_case       )
     UpperCAmelCase_          =							collections.defaultdict(__snake_case       )
     UpperCAmelCase_          =							collections.defaultdict(__snake_case       )
     UpperCAmelCase_          =							collections.defaultdict(__snake_case       )

     # Let's lookup through all transformers object (once).
     for attr_name in dir(__snake_case       ):
          UpperCAmelCase_          =							None
          if attr_name.endswith('''Tokenizer'''       ):
               UpperCAmelCase_          =							slow_tokenizers
               UpperCAmelCase_          =							attr_name[:-9]
          elif attr_name.endswith('''TokenizerFast'''       ):
               UpperCAmelCase_          =							fast_tokenizers
               UpperCAmelCase_          =							attr_name[:-13]
          elif _re_tf_models.match(__snake_case       ) is not None:
               UpperCAmelCase_          =							tf_models
               UpperCAmelCase_          =							_re_tf_models.match(__snake_case       ).groups()[0]
          elif _re_flax_models.match(__snake_case       ) is not None:
               UpperCAmelCase_          =							flax_models
               UpperCAmelCase_          =							_re_flax_models.match(__snake_case       ).groups()[0]
          elif _re_pt_models.match(__snake_case       ) is not None:
               UpperCAmelCase_          =							pt_models
               UpperCAmelCase_          =							_re_pt_models.match(__snake_case       ).groups()[0]

          if lookup_dict is not None:
               while len(__snake_case       ) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase_          =							True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase_          =							''''''.join(camel_case_split(__snake_case       )[:-1]       )

                    # Let's build that table!
     UpperCAmelCase_          =							list(model_name_to_config.keys()       )
     model_names.sort(key=str.lower       )
     UpperCAmelCase_          =							['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     UpperCAmelCase_          =							[len(__snake_case       ) + 2 for c in columns]
     UpperCAmelCase_          =							max([len(__snake_case       ) for name in model_names]       ) + 2

     # Build the table per se
     UpperCAmelCase_          =							'''|''' + '''|'''.join([_center_text(__snake_case   ,				__snake_case       ) for c, w in zip(__snake_case   ,				__snake_case       )]       ) + '''|\n'''
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]       ) + "|\n"

     UpperCAmelCase_          =							{True: '''✅''', False: '''❌'''}
     for name in model_names:
          UpperCAmelCase_          =							model_name_to_prefix[name]
          UpperCAmelCase_          =							[
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(__snake_case   ,				__snake_case       ) for l, w in zip(__snake_case   ,				__snake_case       )]       ) + "|\n"
     return table


def    A						(__A   :    Optional[int]=False       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							_find_text_in_file(
         filename=os.path.join(__snake_case   ,				'''index.md'''       )   ,				start_prompt='''<!--This table is updated automatically from the auto modules'''   ,				end_prompt='''<!-- End table-->'''   ,				)
     UpperCAmelCase_          =							get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(__snake_case   ,				'''index.md'''       )   ,				'''w'''   ,				encoding='''utf-8'''   ,				newline='''\n'''       ) as f:
                    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
          else:
               raise ValueError(
                   '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''       )


if __name__ == "__main__":
    snake_case_ : Tuple    		=       argparse.ArgumentParser()
    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
    snake_case_ : List[Any]    		=       parser.parse_args()

    check_model_table(args.fix_and_overwrite)










from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

