



"""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__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=None			) ->							List[Any]:
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
							A         =					nn.Parameter(lowerCamelCase__			)
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
														A         =					nn.Parameter(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							List[str]:
							# set torch weights for 1-to-1 comparison
							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(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]:
							# set torch weights for 1-to-1 comparison
							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(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]:
							# layernorm 1
							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(lowerCamelCase__			)				,      torch.tensor(lowerCamelCase__			)				,      )

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

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

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

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

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Optional[int]:
							# reformer model
							A         =					torch_model.reformer

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

							if isinstance(weights[3]				,      lowerCamelCase__			):
														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(lowerCamelCase__			)			)

							A         =					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			):
														A         =					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

							# 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(lowerCamelCase__			)				,      torch.tensor(lowerCamelCase__			)				,      )

							# output embeddings
							A         =					np.asarray(weights[9][0]			)
							A         =					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__			) ->							Tuple:
							# Initialise PyTorch model
							A         =					ReformerConfig.from_json_file(lowerCamelCase__			)
							print(f"""Building PyTorch model from configuration: {config}"""			)
							A         =					ReformerModelWithLMHead(lowerCamelCase__			)

							with open(lowerCamelCase__				,      'rb'			) as f:
														A         =					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)








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     ["""pixel_values"""]



		def __init__( self  :    int      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Dict[str, int]] = None      ,							snake_case  :    PILImageResampling = PILImageResampling.BICUBIC      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**snake_case				)
									A         =					size if size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case      ,							default_to_square=snake_case      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					do_rescale
									A         =					do_normalize
									A         =					do_center_crop
									A         =					crop_size
									A         =					size
									A         =					resample
									A         =					rescale_factor
									A         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PILImageResampling.BILINEAR      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)

									if "shortest_edge" in size:
																A         =					get_resize_output_image_size(snake_case      ,							size=size['shortest_edge']      ,							default_to_square=snake_case				)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
																A         =					(size['height'], size['width'])
									else:
																raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""				)
									return resize(snake_case      ,							size=snake_case      ,							resample=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									if "height" not in size or "width" not in size:
																raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
									return center_crop(snake_case      ,							size=(size['height'], size['width'])      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    np.ndarray      ,							snake_case  :    float      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict				)			->						np.ndarray:






									'''simple docstring'''

									return rescale(snake_case      ,							scale=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									return normalize(snake_case      ,							mean=snake_case      ,							std=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    ImageInput      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    int = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[float] = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[str, TensorType]] = None      ,							snake_case  :    Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**snake_case  :    List[str]      ,							)			->						BatchFeature:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									A         =					do_center_crop if do_center_crop is not None else self.do_center_crop
									A         =					crop_size if crop_size is not None else self.crop_size
									A         =					get_size_dict(snake_case      ,							param_name='crop_size'      ,							default_to_square=snake_case				)
									A         =					resample if resample is not None else self.resample
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									A         =					image_mean if image_mean is not None else self.image_mean
									A         =					image_std if image_std is not None else self.image_std

									A         =					size if size is not None else self.size
									A         =					get_size_dict(snake_case				)

									if not is_batched(snake_case				):
																A         =					[images]

									if not valid_images(snake_case				):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'				)

									if do_resize and size is None:
																raise ValueError('Size must be specified if do_resize is True.'				)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'				)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

									# All transformations expect numpy arrays.
									A         =					[to_numpy_array(snake_case				) for image in images]

									if do_resize:
																A         =					[self.resize(image=snake_case      ,							size=snake_case      ,							resample=snake_case				) for image in images]

									if do_center_crop:
																A         =					[self.center_crop(image=snake_case      ,							size=snake_case				) for image in images]

									if do_rescale:
																A         =					[self.rescale(image=snake_case      ,							scale=snake_case				) for image in images]

									if do_normalize:
																A         =					[self.normalize(image=snake_case      ,							mean=snake_case      ,							std=snake_case				) for image in images]

									A         =					[to_channel_dimension_format(snake_case      ,							snake_case				) for image in images]

									A         =					{'pixel_values': images}
									return BatchFeature(data=snake_case      ,							tensor_type=snake_case				)



