



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       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase=None      )   ->					Any:


				"""simple docstring"""

				assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
				snake_case__	:      Union[str, Any]     		=	nn.Parameter(__lowerCAmelCase      )
				if bias is not None:
								assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
								snake_case__	:      Any     		=	nn.Parameter(__lowerCAmelCase      )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					int:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	np.asarray(weights[0]      )
				snake_case__	:      Any     		=	np.asarray(weights[1]      )
				snake_case__	:      List[Any]     		=	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       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      int     		=	np.asarray(weights[0]      )
				snake_case__	:      int     		=	np.asarray(weights[1]      )
				snake_case__	:      List[str]     		=	np.asarray(weights[2]      )
				snake_case__	:      Optional[int]     		=	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       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	weights[0][0][0]
				snake_case__	:      List[Any]     		=	np.asarray(layer_norm_a[0]      )
				snake_case__	:      Any     		=	np.asarray(layer_norm_a[1]      )
				set_param(
				    torch_block.attention.layer_norm  ,  torch.tensor(__lowerCAmelCase      )  ,  torch.tensor(__lowerCAmelCase      )  ,  )

				# lsh weights + output
				snake_case__	:      Tuple     		=	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
				snake_case__	:      Any     		=	weights[2][0][1][2]

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

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

				# intermediate dense
				snake_case__	:      Tuple     		=	np.asarray(intermediate_weights[1][0]      )
				snake_case__	:      Optional[int]     		=	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
				snake_case__	:      Optional[int]     		=	np.asarray(intermediate_weights[4][0]      )
				snake_case__	:      int     		=	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       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	torch_model.reformer

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

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

				snake_case__	:      Optional[int]     		=	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      ):
								snake_case__	:      Dict     		=	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
								set_block_weights_in_torch(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )

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

				# output embeddings
				snake_case__	:      Optional[int]     		=	np.asarray(weights[9][0]      )
				snake_case__	:      List[Any]     		=	np.asarray(weights[9][1]      )
				set_param(
				    torch_model.lm_head.decoder  ,  torch.tensor(__lowerCAmelCase      ).transpose(0  ,  1      ).contiguous()  ,  torch.tensor(__lowerCAmelCase      )  ,  )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	ReformerConfig.from_json_file(__lowerCAmelCase      )
				print(f"""Building PyTorch model from configuration: {config}"""      )
				snake_case__	:      int     		=	ReformerModelWithLMHead(__lowerCAmelCase      )

				with open(__lowerCAmelCase  ,  '''rb'''      ) as f:
								snake_case__	:      Tuple     		=	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 importlib import import_module

from .logging import get_logger


A__								=						get_logger(__name__)


class       a							:



		def __init__(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Any=None  ):
						snake_case__	:      List[str]     		=	attrs or []
						if module is not None:
										for key in module.__dict__:
														if key in attrs or not key.startswith('''__'''  ):
																		setattr(self   ,__lowercase   ,getattr(__lowercase   ,__lowercase  )  )
						snake_case__	:      Optional[Any]     		=	module._original_module if isinstance(__lowercase   ,_PatchedModuleObj  ) else module


class       a							:
		__lowerCAmelCase	:    Any											=					[]



		def __init__(     self     :List[str]   ,__lowercase     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Dict   ,__lowercase     :Any=None  ):
						snake_case__	:      Dict     		=	obj
						snake_case__	:      Dict     		=	target
						snake_case__	:      List[str]     		=	new
						snake_case__	:      int     		=	target.split('''.'''  )[0]
						snake_case__	:      List[str]     		=	{}
						snake_case__	:      Any     		=	attrs or []



		def __enter__(     self     :Tuple  ):
						*snake_case__		, snake_case__	:      str     		=	self.target.split('''.'''  )

						# Patch modules:
						# it's used to patch attributes of submodules like "os.path.join";
						# in this case we need to patch "os" and "os.path"

						for i in range(len(__lowercase  )  ):
										try:
														snake_case__	:      Any     		=	import_module('''.'''.join(submodules[: i + 1]  )  )
										except ModuleNotFoundError:
														continue
										# We iterate over all the globals in self.obj in case we find "os" or "os.path"
										for attr in self.obj.__dir__():
														snake_case__	:      Optional[int]     		=	getattr(self.obj   ,__lowercase  )
														# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
														# This allows to patch renamed modules like "from os import path as ospath".
														if obj_attr is submodule or (
														    (isinstance(__lowercase   ,_PatchedModuleObj  ) and obj_attr._original_module is submodule)
														):
																		snake_case__	:      List[Any]     		=	obj_attr
																		# patch at top level
																		setattr(self.obj   ,__lowercase   ,_PatchedModuleObj(__lowercase   ,attrs=self.attrs  )  )
																		snake_case__	:      List[Any]     		=	getattr(self.obj   ,__lowercase  )
																		# construct lower levels patches
																		for key in submodules[i + 1 :]:
																						setattr(__lowercase   ,__lowercase   ,_PatchedModuleObj(getattr(__lowercase   ,__lowercase   ,__lowercase  )   ,attrs=self.attrs  )  )
																						snake_case__	:      List[Any]     		=	getattr(__lowercase   ,__lowercase  )
																		# finally set the target attribute
																		setattr(__lowercase   ,__lowercase   ,self.new  )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

						if submodules:  # if it's an attribute of a submodule like "os.path.join"
										try:
														snake_case__	:      int     		=	getattr(import_module('''.'''.join(__lowercase  )  )   ,__lowercase  )
										except (AttributeError, ModuleNotFoundError):
														return
										# We iterate over all the globals in self.obj in case we find "os.path.join"
										for attr in self.obj.__dir__():
														# We don't check for the name of the global, but rather if its value *is* "os.path.join".
														# This allows to patch renamed attributes like "from os.path import join as pjoin".
														if getattr(self.obj   ,__lowercase  ) is attr_value:
																		snake_case__	:      str     		=	getattr(self.obj   ,__lowercase  )
																		setattr(self.obj   ,__lowercase   ,self.new  )
						elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
										snake_case__	:      str     		=	globals()['''__builtins__'''][target_attr]
										setattr(self.obj   ,__lowercase   ,self.new  )
						else:
										raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule."""  )



		def __exit__(     self     :Tuple   ,*__lowercase     :Optional[int]  ):
						for attr in list(self.original  ):
										setattr(self.obj   ,__lowercase   ,self.original.pop(__lowercase  )  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						self.__enter__()
						self._active_patches.append(self  )






		def 	__lowerCamelCase			(     self     :Dict  ):
						try:
										self._active_patches.remove(self  )
						except ValueError:
										# If the patch hasn't been started this will fail
										return None

						return self.__exit__()

