






"""simple docstring"""





import argparse
import collections
import importlib.util
import os
import re


# 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/diffusers"""
UpperCamelCase					=     """docs/source/en"""
UpperCamelCase					=     """."""






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       with open(snake_case_		,  "r"		,  encoding="utf-8"		,  newline="\n"						) as f:
              lowercase__:							int         =     f.readlines()
       # Find the start prompt.
       lowercase__:							Union[str, Any]         =     0
       while not lines[start_index].startswith(snake_case_						):
              start_index += 1
       start_index += 1

       lowercase__:							Union[str, Any]         =     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 diffusers module imported is the one in the repo.
UpperCamelCase					=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
UpperCamelCase					=     spec.loader.load_module()






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Union[str, Any]         =     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,  snake_case_						)
       return [m.group(0						) for m in matches]






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							str         =     2 if text == "✅" or text == "❌" else len(snake_case_						)
       lowercase__:							Union[str, Any]         =     (width - text_length) // 2
       lowercase__:							int         =     width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent






def 							A(    ):
       """simple docstring"""





       lowercase__:							List[str]         =     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase__:							Optional[Any]         =     {
           name: config_mapping_names[code]
           for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
           if code in config_mapping_names
       }
       lowercase__:							Optional[Any]         =     {name: config.replace("ConfigMixin"		,  ""						) 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__:							List[str]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Union[str, Any]         =     collections.defaultdict(snake_case_						)
       lowercase__:							Optional[int]         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)
       lowercase__:							int         =     collections.defaultdict(snake_case_						)

       # Let's lookup through all diffusers object (once).
       for attr_name in dir(snake_case_						):
              lowercase__:							Union[str, Any]         =     None
              if attr_name.endswith("Tokenizer"						):
                     lowercase__:							str         =     slow_tokenizers
                     lowercase__:							str         =     attr_name[:-9]
              elif attr_name.endswith("TokenizerFast"						):
                     lowercase__:							Union[str, Any]         =     fast_tokenizers
                     lowercase__:							Optional[int]         =     attr_name[:-13]
              elif _re_tf_models.match(snake_case_						) is not None:
                     lowercase__:							List[str]         =     tf_models
                     lowercase__:							List[Any]         =     _re_tf_models.match(snake_case_						).groups()[0]
              elif _re_flax_models.match(snake_case_						) is not None:
                     lowercase__:							Union[str, Any]         =     flax_models
                     lowercase__:							Optional[int]         =     _re_flax_models.match(snake_case_						).groups()[0]
              elif _re_pt_models.match(snake_case_						) is not None:
                     lowercase__:							Any         =     pt_models
                     lowercase__:							Optional[Any]         =     _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__:							Dict         =     True
                                   break
                            # Try again after removing the last word in the name
                            lowercase__:							List[str]         =     "".join(camel_case_split(snake_case_						)[:-1]						)

    # Let's build that table!
       lowercase__:							str         =     list(model_name_to_config.keys()						)
       model_names.sort(key=str.lower						)
       lowercase__:							Any         =     ["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__:							List[str]         =     [len(snake_case_						) + 2 for c in columns]
       lowercase__:							Optional[int]         =     max([len(snake_case_						) for name in model_names]						) + 2

       # Build the table per se
       lowercase__:							List[Any]         =     "|" + "|".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__:							Dict         =     {True: "✅", False: "❌"}
       for name in model_names:
              lowercase__:							str         =     model_name_to_prefix[name]
              lowercase__:							Union[str, Any]         =     [
                  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(    snake_case_=False						):
       """simple docstring"""





       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__:							Any         =     _find_text_in_file(
           filename=os.path.join(snake_case_		,  "index.mdx"						)		,  start_prompt="<!--This table is updated automatically from the auto modules"		,  end_prompt="<!-- End table-->"		,  )
       lowercase__:							Any         =     get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(snake_case_		,  "index.mdx"						)		,  "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.mdx` 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)








import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_A							=       logging.get_logger(__name__)

_A							=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

       if weight_type is not None:
              UpperCamelCase_											=							getattr(a__						,   a__  ).shape
       else:
              UpperCamelCase_											=							hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
                  f''' {value.shape} for {full_name}'''  )

       if weight_type == "weight":
              UpperCamelCase_											=							value
       elif weight_type == "weight_g":
              UpperCamelCase_											=							value
       elif weight_type == "weight_v":
              UpperCamelCase_											=							value
       elif weight_type == "bias":
              UpperCamelCase_											=							value
       else:
              UpperCamelCase_											=							value

       logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''  )




def 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

       if not is_headless:
              UpperCamelCase_											=							hf_model.dataavec_audio.feature_extractor
              UpperCamelCase_											=							hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              UpperCamelCase_											=							hf_model.feature_extractor
              UpperCamelCase_											=							hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""data2vec_audio.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                            if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
                                   UpperCamelCase_											=							True
                                   if "*" in mapped_key:
                                          UpperCamelCase_											=							name.split(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   if "weight_g" in name:
                                          UpperCamelCase_											=							"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCamelCase_											=							"""weight_v"""
                                   elif "bias" in name:
                                          UpperCamelCase_											=							"""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          UpperCamelCase_											=							"""weight"""
                                   else:
                                          UpperCamelCase_											=							None
                                   set_recursively(a__						,   a__						,   a__						,   a__						,   a__  )
                            continue
              if not is_used:
                     unused_weights.append(a__  )

       logger.warning(f'''Unused weights: {unused_weights}'''  )




def 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							hf_weight.data

       if fsq_value.shape != hf_value.shape:
              raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id == 0:
              UpperCamelCase_											=							"""conv"""
       elif type_id == 2:
              UpperCamelCase_											=							"""layer_norm"""
       else:
              unused_weights.append(a__  )
              return

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id != 0:
              unused_weights.append(a__  )
              return
       else:
              UpperCamelCase_											=							"""conv"""

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              UpperCamelCase_											=							DataaVecAudioModel(a__  )
              UpperCamelCase_											=							os.path.dirname(a__  )

              UpperCamelCase_											=							torch.load(a__  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.weight"""  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.bias"""  )
              UpperCamelCase_											=							os.path.join(a__						,   """converted.pt"""  )
              torch.save(a__						,   a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioForCTC(a__  )
              UpperCamelCase_											=							checkpoint_path

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

       UpperCamelCase_											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""						,   """clean"""						,   split="""validation"""  )
       UpperCamelCase_											=							[x["""array"""] for x in ds[:4]["""audio"""]]

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

       UpperCamelCase_											=							inputs.input_values
       UpperCamelCase_											=							inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """encoder_out"""
              ].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""logits"""]

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

              print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
       else:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """layer_results"""
              ][-1][0].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""last_hidden_state"""]

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   atol=1e-3  )
       print("""Do both models output the same tensors?"""						,   """🔥""" if success else """💩"""  )
       if not success:
              raise Exception("""Something went wRoNg"""  )

       hf_wavavec.save_pretrained(a__  )

       if is_finetuned:
              processor.save_pretrained(a__  )
       else:
              processor.feature_extractor.save_pretrained(a__  )


if __name__ == "__main__":
      _A							=       argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      parser.add_argument(
          '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



