
"""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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case=None	)							->		Optional[int]:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.asarray(weights[0]	)
					_UpperCamelCase							 =  np.asarray(weights[1]	)
					_UpperCamelCase							 =  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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.asarray(weights[0]	)
					_UpperCamelCase							 =  np.asarray(weights[1]	)
					_UpperCamelCase							 =  np.asarray(weights[2]	)
					_UpperCamelCase							 =  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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





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

					# lsh weights + output
					_UpperCamelCase							 =  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
					_UpperCamelCase							 =  weights[2][0][1][2]

					# Chunked Feed Forward
					if len(__lowerCAmelCase	) == 4:
										_UpperCamelCase							 =  intermediate_weights[2]

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

					# intermediate dense
					_UpperCamelCase							 =  np.asarray(intermediate_weights[1][0]	)
					_UpperCamelCase							 =  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
					_UpperCamelCase							 =  np.asarray(intermediate_weights[4][0]	)
					_UpperCamelCase							 =  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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					_UpperCamelCase							 =  torch_model.reformer

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

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

					_UpperCamelCase							 =  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	):
										_UpperCamelCase							 =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
										set_block_weights_in_torch(__lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase	)

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

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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





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

					with open(__lowerCAmelCase,     '''rb'''	) as f:
										_UpperCamelCase							 =  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__":
				_a														=		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														=		parser.parse_args()
				convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
