




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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None       )						->				Union[str, Any]:
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
						_UpperCAmelCase      =			nn.Parameter(snake_case       )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
												_UpperCAmelCase      =			nn.Parameter(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				str:
						# set torch weights for 1-to-1 comparison
						_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(snake_case       ).transpose(1			,     2       ).contiguous().view(-1			,     snake_case       )			,     )
						set_param(
						    torch_layer.self_attention.value			,     torch.tensor(snake_case       ).transpose(1			,     2       ).contiguous().view(-1			,     snake_case       )			,     )
						set_param(
						    torch_layer.output.dense			,     torch.tensor(snake_case       ).view(-1			,     snake_case       ).contiguous().transpose(0			,     1       )			,     )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Tuple:
						# set torch weights for 1-to-1 comparison
						_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(snake_case       ).transpose(1			,     2       ).contiguous().view(-1			,     snake_case       )			,     )
						set_param(
						    torch_layer.self_attention.key			,     torch.tensor(snake_case       ).transpose(1			,     2       ).contiguous().view(-1			,     snake_case       )			,     )
						set_param(
						    torch_layer.self_attention.value			,     torch.tensor(snake_case       ).transpose(1			,     2       ).contiguous().view(-1			,     snake_case       )			,     )
						set_param(
						    torch_layer.output.dense			,     torch.tensor(snake_case       ).view(-1			,     snake_case       ).contiguous().transpose(0			,     1       )			,     )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Tuple:
						# layernorm 1
						_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(snake_case       )			,     torch.tensor(snake_case       )			,     )

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

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

						# Chunked Feed Forward
						if len(snake_case       ) == 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(snake_case       )			,     torch.tensor(snake_case       )			,     )

						# 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(snake_case       ).transpose(0			,     1       ).contiguous()			,     torch.tensor(snake_case       )			,     )

						# 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(snake_case       ).transpose(0			,     1       ).contiguous()			,     torch.tensor(snake_case       )			,     )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Dict:
						# reformer model
						_UpperCAmelCase      =			torch_model.reformer

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

						if isinstance(weights[3]			,     snake_case       ):
												_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(snake_case       )       )

						_UpperCAmelCase      =			weights[5]
						assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
						    snake_case       ), "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(snake_case			,     snake_case			,     snake_case       )

						# 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(snake_case       )			,     torch.tensor(snake_case       )			,     )

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Union[str, Any]:
						# Initialise PyTorch model
						_UpperCAmelCase      =			ReformerConfig.from_json_file(snake_case       )
						print(f"Building PyTorch model from configuration: {config}"       )
						_UpperCAmelCase      =			ReformerModelWithLMHead(snake_case       )

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

						set_model_weights_in_torch(snake_case			,     snake_case			,     config.hidden_size       )

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


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)





import os
import sys
import unittest


a													=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a													=			os.path.join(git_repo_path, "src", "transformers")

a													=			"\n{0} = None\n"

a													=			"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


a													=			"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""			)
								self.assertIsNone(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			find_backend("""    if not is_tokenizers_available():"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""tokenizers"""			)

								_UpperCAmelCase      =			find_backend("""    if not is_tensorflow_text_available():"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""tensorflow_text"""			)

								_UpperCAmelCase      =			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tokenizers"""			)

								_UpperCAmelCase      =			find_backend(
								    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tensorflow_text"""			)

								_UpperCAmelCase      =			find_backend(
								    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tokenizers_and_vision"""			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn("""torch"""						,		_SCREAMING_SNAKE_CASE			)
								self.assertIn("""tensorflow_text"""						,		_SCREAMING_SNAKE_CASE			)
								self.assertIn("""sentencepiece_and_tokenizers"""						,		_SCREAMING_SNAKE_CASE			)

								# Likewise, we can't assert on the exact content of a key
								self.assertIn("""BertModel"""						,		objects["""torch"""]			)
								self.assertIn("""TFBertModel"""						,		objects["""tf"""]			)
								self.assertIn("""FlaxBertModel"""						,		objects["""flax"""]			)
								self.assertIn("""BertModel"""						,		objects["""torch"""]			)
								self.assertIn("""TFBertTokenizer"""						,		objects["""tensorflow_text"""]			)
								self.assertIn("""convert_slow_tokenizer"""						,		objects["""sentencepiece_and_tokenizers"""]			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			create_dummy_object("""CONSTANT"""						,		"""'torch'"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""\nCONSTANT = None\n"""			)

								_UpperCAmelCase      =			create_dummy_object("""function"""						,		"""'torch'"""			)
								self.assertEqual(
								    _SCREAMING_SNAKE_CASE						,		"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""			)

								_UpperCAmelCase      =			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
								_UpperCAmelCase      =			create_dummy_object("""FakeClass"""						,		"""'torch'"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
								_UpperCAmelCase      =			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}			)
								self.assertEqual(dummy_files["""torch"""]						,		_SCREAMING_SNAKE_CASE			)