"""simple docstring"""




import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


__lowerCAmelCase    :							Any													=						"<<<<<<< This should probably be modified because it mentions: "

__lowerCAmelCase    :							Any													=						"=======\n>>>>>>>\n"

__lowerCAmelCase    :							str													=						[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

__lowerCAmelCase    :							Union[str, Any]													=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"tfds\.core", R"datasets"),
    (R"tf\.io\.gfile\.GFile", R"open"),
    (R"tf\.([\w\d]+)", R"datasets.Value(\'\1\')"),
    (R"tfds\.features\.Text\(\)", R"datasets.Value(\'string\')"),
    (R"tfds\.features\.Text\(", R"datasets.Value(\'string\'),"),
    (R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
    (R"tfds\.features\.FeaturesDict\(", R"dict("),
    (R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
    (R"tfds\.", R"datasets."),
    (R"dl_manager\.manual_dir", R"self.config.data_dir"),
    (R"self\.builder_config", R"self.config"),
]







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return ConvertCommand(args.tfds_path			,  args.datasets_directory     )






class       a_      (     lowerCAmelCase__			):




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   parser.add_parser(
							    """convert"""  ,						help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""  ,						)
							train_parser.add_argument(
							    """--tfds_path"""  ,						type=_lowerCamelCase  ,						required=_lowerCamelCase  ,						help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""  ,						)
							train_parser.add_argument(
							    """--datasets_directory"""  ,						type=_lowerCamelCase  ,						required=_lowerCamelCase  ,						help="""Path to the HuggingFace Datasets folder."""     )
							train_parser.set_defaults(func=_lowerCamelCase     )




					def __init__(	self						:     Dict  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     str  ,						*snake_case__						:     Any     ):
							lowerCAmelCase__        =   get_logger("""datasets-cli/converting"""     )

							lowerCAmelCase__        =   tfds_path
							lowerCAmelCase__        =   datasets_directory




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):





							if os.path.isdir(self._tfds_path     ):
									lowerCAmelCase__        =   os.path.abspath(self._tfds_path     )
							elif os.path.isfile(self._tfds_path     ):
									lowerCAmelCase__        =   os.path.dirname(self._tfds_path     )
							else:
									raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""     )

							lowerCAmelCase__        =   os.path.abspath(self._datasets_directory     )

							self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""     )

							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   {}

							if os.path.isdir(self._tfds_path     ):
									lowerCAmelCase__        =   os.listdir(_lowerCamelCase     )
							else:
									lowerCAmelCase__        =   [os.path.basename(self._tfds_path     )]

							for f_name in file_names:
									self._logger.info(F"""Looking at file {f_name}"""     )
									lowerCAmelCase__        =   os.path.join(_lowerCamelCase  ,						_lowerCamelCase     )
									lowerCAmelCase__        =   os.path.join(_lowerCamelCase  ,						_lowerCamelCase     )

									if not os.path.isfile(_lowerCamelCase     ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
											self._logger.info("""Skipping file"""     )
											continue

									with open(_lowerCamelCase  ,						encoding="""utf-8"""     ) as f:
											lowerCAmelCase__        =   f.readlines()

									lowerCAmelCase__        =   []
									lowerCAmelCase__        =   False
									lowerCAmelCase__        =   False
									lowerCAmelCase__        =   []
									for line in lines:
											lowerCAmelCase__        =   line

											# Convert imports
											if "import tensorflow.compat.v2 as tf" in out_line:
													continue
											elif "@tfds.core" in out_line:
													continue
											elif "builder=self" in out_line:
													continue
											elif "import tensorflow_datasets.public_api as tfds" in out_line:
													lowerCAmelCase__        =   """import datasets\n"""
											elif "import tensorflow" in out_line:
													# order is important here
													lowerCAmelCase__        =   """"""
													continue
											elif "from absl import logging" in out_line:
													lowerCAmelCase__        =   """from datasets import logging\n"""
											elif "getLogger" in out_line:
													lowerCAmelCase__        =   out_line.replace("""getLogger"""  ,						"""get_logger"""     )
											elif any(expression in out_line for expression in TO_HIGHLIGHT     ):
													lowerCAmelCase__        =   True
													lowerCAmelCase__        =   list(filter(lambda snake_case__     : e in out_line  ,						_lowerCamelCase     )     )
													out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_lowerCamelCase     ) + """\n"""     )
													out_lines.append(_lowerCamelCase     )
													out_lines.append(_lowerCamelCase     )
													continue
											else:
													for pattern, replacement in TO_CONVERT:
															lowerCAmelCase__        =   re.sub(_lowerCamelCase  ,						_lowerCamelCase  ,						_lowerCamelCase     )

                # Take care of saving utilities (to later move them together with main script)
											if "tensorflow_datasets" in out_line:
													lowerCAmelCase__        =   re.match(R"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""  ,						_lowerCamelCase     )
													tfds_imports.extend(imp.strip() for imp in match.group(1     ).split(""","""     )     )
													lowerCAmelCase__        =   """from . import """ + match.group(1     )

											# Check we have not forget anything
											if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
													raise ValueError(F"""Error converting {out_line.strip()}"""     )

											if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
													lowerCAmelCase__        =   True
											out_lines.append(_lowerCamelCase     )

									if is_builder or "wmt" in f_name:
											# We create a new directory for each dataset
											lowerCAmelCase__        =   f_name.replace(""".py"""  ,						""""""     )
											lowerCAmelCase__        =   os.path.join(_lowerCamelCase  ,						_lowerCamelCase     )
											lowerCAmelCase__        =   os.path.join(_lowerCamelCase  ,						_lowerCamelCase     )
											os.makedirs(_lowerCamelCase  ,						exist_ok=_lowerCamelCase     )
											self._logger.info(F"""Adding directory {output_dir}"""     )
											imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}     )
									else:
											# Utilities will be moved at the end
											utils_files.append(_lowerCamelCase     )

									if needs_manual_update:
											with_manual_update.append(_lowerCamelCase     )

									with open(_lowerCamelCase  ,						"""w"""  ,						encoding="""utf-8"""     ) as f:
											f.writelines(_lowerCamelCase     )
									self._logger.info(F"""Converted in {output_file}"""     )

							for utils_file in utils_files:
									try:
											lowerCAmelCase__        =   os.path.basename(_lowerCamelCase     )
											lowerCAmelCase__        =   imports_to_builder_map[f_name.replace(""".py"""  ,						""""""     )]
											self._logger.info(F"""Moving {dest_folder} to {utils_file}"""     )
											shutil.copy(_lowerCamelCase  ,						_lowerCamelCase     )
									except KeyError:
											self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""     )

							if with_manual_update:
									for file_path in with_manual_update:
											self._logger.warning(
											    F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""     )







"""simple docstring"""




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
		lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
				lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )

		set_param(
		    torch_layer.self_attention.query_key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )
		lowerCAmelCase__        =   np.asarray(weights[3]     )

		set_param(
		    torch_layer.self_attention.query			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   weights[0][0][0]
		lowerCAmelCase__        =   np.asarray(layer_norm_a[0]     )
		lowerCAmelCase__        =   np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# lsh weights + output
		lowerCAmelCase__        =   weights[0][1]
		if len(lowerCamelCase__     ) < 4:
				set_layer_weights_in_torch_lsh(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )
		else:
				set_layer_weights_in_torch_local(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )

		# intermediate weighs
		lowerCAmelCase__        =   weights[2][0][1][2]

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

		# layernorm 2
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][1]     )
		set_param(
		    torch_block.feed_forward.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate dense
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][1]     )
		set_param(
		    torch_block.feed_forward.dense.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate out
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][1]     )
		set_param(
		    torch_block.feed_forward.output.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

		# word embeds
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		set_param(
		    torch_model_reformer.embeddings.word_embeddings			,  torch.tensor(lowerCamelCase__     )			,  )

		if isinstance(weights[3]			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights     )     ):
						lowerCAmelCase__        =   np.asarray(weights[3][emb_idx][0]     )
						assert (
						    position_embeddings.weights[emb_idx].shape == emb_weights.shape
						), f"""{position_embeddings[emb_idx]} emb does not match"""
						lowerCAmelCase__        =   nn.Parameter(torch.tensor(lowerCamelCase__     )     )

		lowerCAmelCase__        =   weights[5]
		assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
		    lowerCamelCase__     ), "HF and trax model do not have the same number of layers"
		for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers     ):
				lowerCAmelCase__        =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# output layer norm
		lowerCAmelCase__        =   np.asarray(weights[7][0]     )
		lowerCAmelCase__        =   np.asarray(weights[7][1]     )
		set_param(
		    torch_model_reformer.encoder.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# output embeddings
		lowerCAmelCase__        =   np.asarray(weights[9][0]     )
		lowerCAmelCase__        =   np.asarray(weights[9][1]     )
		set_param(
		    torch_model.lm_head.decoder			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ReformerConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   ReformerModelWithLMHead(lowerCamelCase__     )

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   pickle.load(lowerCamelCase__     )["""weights"""]

		set_model_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  config.hidden_size     )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained Reformer model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




