


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
UpperCAmelCase__   	=							'''src/transformers'''
UpperCAmelCase__   	=							'''docs/source/en'''
UpperCAmelCase__   	=							'''.'''
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


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

 _lowercase							      =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 |
UpperCAmelCase__   	=							'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase__   	=							re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
UpperCAmelCase__   	=							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.
UpperCAmelCase__   	=							re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__   	=							direct_transformers_import(TRANSFORMERS_PATH)
def 				UpperCAmelCase_					(							__snake_case						)     ->			Tuple:







 """simple docstring"""


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







 """simple docstring"""


 _lowercase							      =2 if text == '''✅''' or text == '''❌''' else len(__snake_case						)
 _lowercase							      =(width - text_length) // 2
 _lowercase							      =width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent
def 				UpperCAmelCase_					(							)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 _lowercase							      ={
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 _lowercase							      ={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.
 _lowercase							      =collections.defaultdict(__snake_case						)
 _lowercase							      =collections.defaultdict(__snake_case						)
 _lowercase							      =collections.defaultdict(__snake_case						)
 _lowercase							      =collections.defaultdict(__snake_case						)
 _lowercase							      =collections.defaultdict(__snake_case						)

 # Let's lookup through all transformers object (once).
 for attr_name in dir(__snake_case						):
  _lowercase							      =None
  if attr_name.endswith('''Tokenizer'''						):
   _lowercase							      =slow_tokenizers
   _lowercase							      =attr_name[:-9]
  elif attr_name.endswith('''TokenizerFast'''						):
   _lowercase							      =fast_tokenizers
   _lowercase							      =attr_name[:-13]
  elif _re_tf_models.match(__snake_case						) is not None:
   _lowercase							      =tf_models
   _lowercase							      =_re_tf_models.match(__snake_case						).groups()[0]
  elif _re_flax_models.match(__snake_case						) is not None:
   _lowercase							      =flax_models
   _lowercase							      =_re_flax_models.match(__snake_case						).groups()[0]
  elif _re_pt_models.match(__snake_case						) is not None:
   _lowercase							      =pt_models
   _lowercase							      =_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():
     _lowercase							      =True
     break
    # Try again after removing the last word in the name
    _lowercase							      =''''''.join(camel_case_split(__snake_case						)[:-1]						)

    # Let's build that table!
 _lowercase							      =list(model_name_to_config.keys()						)
 model_names.sort(key=str.lower						)
 _lowercase							      =['''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).
 _lowercase							      =[len(__snake_case						) + 2 for c in columns]
 _lowercase							      =max([len(__snake_case						) for name in model_names]						) + 2

 # Build the table per se
 _lowercase							      ='''|''' + '''|'''.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"

 _lowercase							      ={True: '''✅''', False: '''❌'''}
 for name in model_names:
  _lowercase							      =model_name_to_prefix[name]
  _lowercase							      =[
      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 				UpperCAmelCase_					(							__snake_case=False						)     ->			List[str]:







 """simple docstring"""


 _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =_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-->'''     ,						)
 _lowercase							      =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__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  UpperCAmelCase__   	=							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       )

