





'''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None   )    ->       int:

   '''simple docstring'''







   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[2]   )

   set_param(
       torch_layer.self_attention.query_key				,						torch.tensor(__UpperCAmelCase   ).transpose(1				,						2   ).contiguous().view(-1				,						__UpperCAmelCase   )				,						)
   set_param(
       torch_layer.self_attention.value				,						torch.tensor(__UpperCAmelCase   ).transpose(1				,						2   ).contiguous().view(-1				,						__UpperCAmelCase   )				,						)
   set_param(
       torch_layer.output.dense				,						torch.tensor(__UpperCAmelCase   ).view(-1				,						__UpperCAmelCase   ).contiguous().transpose(0				,						1   )				,						)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[2]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[3]   )

   set_param(
       torch_layer.self_attention.query				,						torch.tensor(__UpperCAmelCase   ).transpose(1				,						2   ).contiguous().view(-1				,						__UpperCAmelCase   )				,						)
   set_param(
       torch_layer.self_attention.key				,						torch.tensor(__UpperCAmelCase   ).transpose(1				,						2   ).contiguous().view(-1				,						__UpperCAmelCase   )				,						)
   set_param(
       torch_layer.self_attention.value				,						torch.tensor(__UpperCAmelCase   ).transpose(1				,						2   ).contiguous().view(-1				,						__UpperCAmelCase   )				,						)
   set_param(
       torch_layer.output.dense				,						torch.tensor(__UpperCAmelCase   ).view(-1				,						__UpperCAmelCase   ).contiguous().transpose(0				,						1   )				,						)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							weights[0][0][0]
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[1]   )
   set_param(
       torch_block.attention.layer_norm				,						torch.tensor(__UpperCAmelCase   )				,						torch.tensor(__UpperCAmelCase   )				,						)

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

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

   # Chunked Feed Forward
   if len(__UpperCAmelCase   ) == 4:
      __SCREAMING_SNAKE_CASE   				=							intermediate_weights[2]

   # layernorm 2
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[0][0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[0][1]   )
   set_param(
       torch_block.feed_forward.layer_norm				,						torch.tensor(__UpperCAmelCase   )				,						torch.tensor(__UpperCAmelCase   )				,						)

   # intermediate dense
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[1][0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[1][1]   )
   set_param(
       torch_block.feed_forward.dense.dense				,						torch.tensor(__UpperCAmelCase   ).transpose(0				,						1   ).contiguous()				,						torch.tensor(__UpperCAmelCase   )				,						)

   # intermediate out
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[4][0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(intermediate_weights[4][1]   )
   set_param(
       torch_block.feed_forward.output.dense				,						torch.tensor(__UpperCAmelCase   ).transpose(0				,						1   ).contiguous()				,						torch.tensor(__UpperCAmelCase   )				,						)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch_model.reformer

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

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

   __SCREAMING_SNAKE_CASE   				=							weights[5]
   assert len(torch_model_reformer.encoder.layers   ) * 4 == len(
       __UpperCAmelCase   ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers   ):
      __SCREAMING_SNAKE_CASE   				=							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   # output layer norm
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[7][0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[7][1]   )
   set_param(
       torch_model_reformer.encoder.layer_norm				,						torch.tensor(__UpperCAmelCase   )				,						torch.tensor(__UpperCAmelCase   )				,						)

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ReformerConfig.from_json_file(__UpperCAmelCase   )
   print(f"""Building PyTorch model from configuration: {config}"""   )
   __SCREAMING_SNAKE_CASE   				=							ReformerModelWithLMHead(__UpperCAmelCase   )

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

   set_model_weights_in_torch(__UpperCAmelCase				,						__UpperCAmelCase				,						config.hidden_size   )

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


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, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		return [
		    int(1_000 * (box[0] / width)     ),
		    int(1_000 * (box[1] / height)     ),
		    int(1_000 * (box[2] / width)     ),
		    int(1_000 * (box[3] / height)     ),
		]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			to_pil_image(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			pil_image.size
		SCREAMING_SNAKE_CASE_    			=			pytesseract.image_to_data(__a,				lang=__a,				output_type='''dict''',				config=__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

		# filter empty words and corresponding coordinates
		SCREAMING_SNAKE_CASE_    			=			[idx for idx, word in enumerate(__a     ) if not word.strip()]
		SCREAMING_SNAKE_CASE_    			=			[word for idx, word in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]

		# turn coordinates into (left, top, left+width, top+height) format
		SCREAMING_SNAKE_CASE_    			=			[]
		for x, y, w, h in zip(__a,				__a,				__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			[x, y, x + w, y + h]
				actual_boxes.append(__a     )

		# finally, normalize the bounding boxes
		SCREAMING_SNAKE_CASE_    			=			[]
		for box in actual_boxes:
				normalized_boxes.append(normalize_box(__a,				__a,				__a     )     )

		assert len(__a     ) == len(__a     ), "Not as many words as there are bounding boxes"

		return words, normalized_boxes





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ""							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			do_resize
									SCREAMING_SNAKE_CASE_    			=			size
									SCREAMING_SNAKE_CASE_    			=			resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_value
									SCREAMING_SNAKE_CASE_    			=			do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else IMAGENET_STANDARD_STD
									SCREAMING_SNAKE_CASE_    			=			apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									if "height" not in size or "width" not in size:
											raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'     )
									SCREAMING_SNAKE_CASE_    			=			(size['''height'''], size['''width'''])
									return resize(SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_							,							resample=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else self.size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_    			=			apply_ocr if apply_ocr is not None else self.apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang if ocr_lang is not None else self.ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config if tesseract_config is not None else self.tesseract_config

									SCREAMING_SNAKE_CASE_    			=			make_list_of_images(SCREAMING_SNAKE_CASE_     )

									if not valid_images(SCREAMING_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_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('''If do_normalize is True, image_mean and image_std must be specified.'''     )

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

									# Tesseract OCR to get words + normalized bounding boxes
									if apply_ocr:
											requires_backends(self							,							'''pytesseract'''     )
											SCREAMING_SNAKE_CASE_    			=			[]
											SCREAMING_SNAKE_CASE_    			=			[]
											for image in images:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			apply_tesseract(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
													words_batch.append(SCREAMING_SNAKE_CASE_     )
													boxes_batch.append(SCREAMING_SNAKE_CASE_     )

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

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

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

									SCREAMING_SNAKE_CASE_    			=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) for image in images]

									SCREAMING_SNAKE_CASE_    			=			BatchFeature(data={'''pixel_values''': images}							,							tensor_type=SCREAMING_SNAKE_CASE_     )

									if apply_ocr:
											SCREAMING_SNAKE_CASE_    			=			words_batch
											SCREAMING_SNAKE_CASE_    			=			boxes_batch
									return data