
"""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       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     int=None     ):
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
	A						   =   nn.Parameter(snake_case__     )
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
		A						   =   nn.Parameter(snake_case__     )






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Optional[int]	,      snake_case__   :     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(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       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Optional[int]	,      snake_case__   :     Dict     ):
	# 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(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       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     List[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(snake_case__     )	,      torch.tensor(snake_case__     )	,      )

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

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

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

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






def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     List[Any]     ):
	# reformer model
	A						   =   torch_model.reformer

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

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

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

	# 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(snake_case__     )	,      torch.tensor(snake_case__     )	,      )

	# output embeddings
	A						   =   np.asarray(weights[9][0]     )
	A						   =   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       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     List[Any]	,      snake_case__   :     Any     ):
	# Initialise PyTorch model
	A						   =   ReformerConfig.from_json_file(snake_case__     )
	print(F'Building PyTorch model from configuration: {config}'     )
	A						   =   ReformerModelWithLMHead(snake_case__     )

	with open(snake_case__	,      'rb'     ) as f:
		A						   =   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__":
		_lowercase							   =				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.'''
		)
		_lowercase							   =				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, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(A_  ,size=size['shortest_edge']  ,default_to_square=A_      )
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
							return center_crop(A_  ,size=(size['height'], size['width'])  ,data_format=A_  ,**A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Any  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							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(A_  ,param_name='crop_size'      )
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							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						   =   make_list_of_images(A_      )

							if not valid_images(A_      ):
								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.'      )

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

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

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

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

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

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

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

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
								if len(A_      ) != len(A_      ):
									raise ValueError(
									    'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

								for idx in range(len(A_      )      ):
									A						   =   torch.nn.functional.interpolate(
									    logits[idx].unsqueeze(dim=0      )  ,size=target_sizes[idx]  ,mode='bilinear'  ,align_corners=A_      )
									A						   =   resized_logits[0].argmax(dim=0      )
									semantic_segmentation.append(A_      )
							else:
								A						   =   logits.argmax(dim=1      )
								A						   =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

							return semantic_segmentation