
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 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						start_index
  while not lines[end_index].startswith(_a  ):
    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 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  _lowerCamelCase      =						{
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  _lowerCamelCase      =						{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.
  _lowerCamelCase      =						collections.defaultdict(_a  )
  _lowerCamelCase      =						collections.defaultdict(_a  )
  _lowerCamelCase      =						collections.defaultdict(_a  )
  _lowerCamelCase      =						collections.defaultdict(_a  )
  _lowerCamelCase      =						collections.defaultdict(_a  )

  # Let's lookup through all diffusers object (once).
  for attr_name in dir(_a  ):
    _lowerCamelCase      =						None
    if attr_name.endswith('''Tokenizer'''  ):
      _lowerCamelCase      =						slow_tokenizers
      _lowerCamelCase      =						attr_name[:-9]
    elif attr_name.endswith('''TokenizerFast'''  ):
      _lowerCamelCase      =						fast_tokenizers
      _lowerCamelCase      =						attr_name[:-1_3]
    elif _re_tf_models.match(_a  ) is not None:
      _lowerCamelCase      =						tf_models
      _lowerCamelCase      =						_re_tf_models.match(_a  ).groups()[0]
    elif _re_flax_models.match(_a  ) is not None:
      _lowerCamelCase      =						flax_models
      _lowerCamelCase      =						_re_flax_models.match(_a  ).groups()[0]
    elif _re_pt_models.match(_a  ) is not None:
      _lowerCamelCase      =						pt_models
      _lowerCamelCase      =						_re_pt_models.match(_a  ).groups()[0]

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

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

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

  _lowerCamelCase      =						{True: "✅", False: "❌"}
  for name in model_names:
    _lowerCamelCase      =						model_name_to_prefix[name]
    _lowerCamelCase      =						[
        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(_a			,   _a  ) for l, w in zip(_a			,   _a  )]  ) + "|\n"
  return table


def 		_lowerCamelCase       (   _a=False  ):
  """simple docstring"""




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

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(_a			,   '''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)






"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



