





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   __lowerCamelCase   (				__a  :List[str]       ,	__a  :Any       ,	__a  :Tuple=None       )						->							Dict:






		"""simple docstring"""

		assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
		A__     =  nn.Parameter(__a       )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
				A__     =  nn.Parameter(__a       )







def   __lowerCamelCase   (				__a  :List[Any]       ,	__a  :Tuple       ,	__a  :int       )						->							Dict:






		"""simple docstring"""

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

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







def   __lowerCamelCase   (				__a  :List[Any]       ,	__a  :Optional[Any]       ,	__a  :List[Any]       )						->							Union[str, Any]:






		"""simple docstring"""

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

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







def   __lowerCamelCase   (				__a  :str       ,	__a  :Dict       ,	__a  :Union[str, Any]       )						->							Optional[int]:






		"""simple docstring"""

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

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

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

		# Chunked Feed Forward
		if len(__a       ) == 4:
				A__     =  intermediate_weights[2]

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

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

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







def   __lowerCamelCase   (				__a  :List[str]       ,	__a  :Union[str, Any]       ,	__a  :Union[str, Any]       )						->							Optional[Any]:






		"""simple docstring"""

		A__     =  torch_model.reformer

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

		if isinstance(weights[3]       ,	__a       ):
				A__     =  torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights       )       ):
						A__     =  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'
						A__     =  nn.Parameter(torch.tensor(__a       )       )

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

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

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







def   __lowerCamelCase   (				__a  :Any       ,	__a  :Optional[Any]       ,	__a  :Union[str, Any]       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  ReformerConfig.from_json_file(__a       )
		print(F'Building PyTorch model from configuration: {config}'       )
		A__     =  ReformerModelWithLMHead(__a       )

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

		set_model_weights_in_torch(__a       ,	__a       ,	config.hidden_size       )

		# Save pytorch-model
		print(F'Save PyTorch model to {pytorch_dump_path}'       )
		torch.save(model.state_dict()       ,	__a       )


if __name__ == "__main__":
					A       :   Any					= 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.'''
					)
					A       :   Optional[Any]					= parser.parse_args()
					convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			def __init__(     self  :       Any				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.sql.Selectable"]				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,      __lowerCAmelCase  :       Optional[Features] = None				,      __lowerCAmelCase  :       str = None				,      __lowerCAmelCase  :       bool = False				,      **__lowerCAmelCase  :       Dict				,      )      ->      Any:







					"""simple docstring"""






					super().__init__(features=__lowerCAmelCase				,      cache_dir=__lowerCAmelCase				,      keep_in_memory=__lowerCAmelCase				,      **__lowerCAmelCase  )
					A__     =  Sql(
					    cache_dir=__lowerCAmelCase				,      features=__lowerCAmelCase				,      sql=__lowerCAmelCase				,      con=__lowerCAmelCase				,      **__lowerCAmelCase				,      )







			def   a_			(     self  :       Optional[int]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  None
					A__     =  None
					A__     =  None
					A__     =  None

					self.builder.download_and_prepare(
					    download_config=__lowerCAmelCase				,      download_mode=__lowerCAmelCase				,      verification_mode=__lowerCAmelCase				,      base_path=__lowerCAmelCase				,      )

					# Build dataset for splits
					A__     =  self.builder.as_dataset(
					    split="""train"""				,      verification_mode=__lowerCAmelCase				,      in_memory=self.keep_in_memory  )
					return dataset


class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Dataset				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       Optional[int] = None				,      **__lowerCAmelCase  :       List[Any]				,      )      ->      Any:







					"""simple docstring"""






					if num_proc is not None and num_proc <= 0:
							raise ValueError(f'num_proc {num_proc} must be an integer > 0.'  )

					A__     =  dataset
					A__     =  name
					A__     =  con
					A__     =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					A__     =  num_proc
					A__     =  to_sql_kwargs






			def   a_			(     self  :       Any  )      ->      int:







					"""simple docstring"""






					A__     =  self.to_sql_kwargs.pop("""sql"""				,      __lowerCAmelCase  )
					A__     =  self.to_sql_kwargs.pop("""con"""				,      __lowerCAmelCase  )
					A__     =  self.to_sql_kwargs.pop("""index"""				,      __lowerCAmelCase  )

					A__     =  self._write(index=__lowerCAmelCase				,      **self.to_sql_kwargs  )
					return written






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__ ,     A__ ,     A__     =  args
					A__     =  {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
					A__     =  query_table(
					    table=self.dataset.data				,      key=slice(__lowerCAmelCase				,      offset + self.batch_size  )				,      indices=self.dataset._indices				,      )
					A__     =  batch.to_pandas()
					A__     =  df.to_sql(self.name				,      self.con				,      index=__lowerCAmelCase				,      **__lowerCAmelCase  )
					return num_rows or len(__lowerCAmelCase  )







			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       Dict  )      ->      int:







					"""simple docstring"""






					A__     =  0

					if self.num_proc is None or self.num_proc == 1:
							for offset in logging.tqdm(
							    range(0				,      len(self.dataset  )				,      self.batch_size  )				,      unit="""ba"""				,      disable=not logging.is_progress_bar_enabled()				,      desc="""Creating SQL from Arrow format"""				,      ):
									written += self._batch_sql((offset, index, to_sql_kwargs)  )
					else:
							A__ ,     A__     =  len(self.dataset  ), self.batch_size
							with multiprocessing.Pool(self.num_proc  ) as pool:
									for num_rows in logging.tqdm(
									    pool.imap(
									        self._batch_sql				,      [(offset, index, to_sql_kwargs) for offset in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  )]				,      )				,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,      unit="""ba"""				,      disable=not logging.is_progress_bar_enabled()				,      desc="""Creating SQL from Arrow format"""				,      ):
											written += num_rows

					return written



