




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    (							lowercase			: Any			,							lowercase			: Tuple			,							lowercase			: List[Any]=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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Tuple			,							lowercase			: Any  ):


						'''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     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Tuple			,							lowercase			: Optional[Any]  ):


						'''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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Optional[int]			,							lowercase			: Optional[int]  ):


						'''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     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Optional[int]			,							lowercase			: Optional[int]  ):


						'''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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Optional[int]			,							lowercase			: int  ):


						'''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       :	Dict									=     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       :	Optional[Any]									=     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_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



