'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=7     ,			_lowerCamelCase=3     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=1_8     ,			_lowerCamelCase=3_0     ,			_lowerCamelCase=4_0_0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=[0.5, 0.5, 0.5]     ,			_lowerCamelCase=[0.5, 0.5, 0.5]     ,			_lowerCamelCase=None     ,			):
         __UpperCamelCase	:	int		    =  size if size is not None else {'shortest_edge': 1_8}
         __UpperCamelCase	:	List[str]		    =  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

         __UpperCamelCase	:	List[Any]		    =  parent
         __UpperCamelCase	:	Union[str, Any]		    =  batch_size
         __UpperCamelCase	:	Optional[int]		    =  num_channels
         __UpperCamelCase	:	str		    =  num_frames
         __UpperCamelCase	:	Any		    =  image_size
         __UpperCamelCase	:	Union[str, Any]		    =  min_resolution
         __UpperCamelCase	:	Optional[int]		    =  max_resolution
         __UpperCamelCase	:	Optional[int]		    =  do_resize
         __UpperCamelCase	:	Optional[int]		    =  size
         __UpperCamelCase	:	Dict		    =  do_normalize
         __UpperCamelCase	:	Union[str, Any]		    =  image_mean
         __UpperCamelCase	:	Dict		    =  image_std
         __UpperCamelCase	:	Dict		    =  crop_size







     def lowerCAmelCase     (		self    ):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_size": self.crop_size,
         }






@require_torch
@require_vision
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VivitImageProcessor if is_vision_available() else None







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  VivitImageProcessingTester(self    )







     @property
     def lowerCAmelCase     (		self    ):
         return self.image_processor_tester.prepare_image_processor_dict()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'image_mean'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'image_std'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'do_normalize'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'do_resize'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'do_center_crop'    )    )
         self.assertTrue(hasattr(_lowerCamelCase     ,			'size'    )    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size     ,			{'shortest_edge': 1_8}    )
         self.assertEqual(image_processor.crop_size     ,			{'height': 1_8, 'width': 1_8}    )

         __UpperCamelCase	:	Optional[int]		    =  self.image_processing_class.from_dict(self.image_processor_dict     ,			size=4_2     ,			crop_size=8_4    )
         self.assertEqual(image_processor.size     ,			{'shortest_edge': 4_2}    )
         self.assertEqual(image_processor.crop_size     ,			{'height': 8_4, 'width': 8_4}    )







     def lowerCAmelCase     (		self    ):
         # Initialize image_processing
         __UpperCamelCase	:	List[str]		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random PIL videos
         __UpperCamelCase	:	Tuple		    =  prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=_lowerCamelCase    )
         for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertIsInstance(video[0]     ,			Image.Image    )

         # Test not batched input
         __UpperCamelCase	:	List[Any]		    =  image_processing(video_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	Tuple		    =  image_processing(_lowerCamelCase     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)







     def lowerCAmelCase     (		self    ):
         # Initialize image_processing
         __UpperCamelCase	:	Tuple		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         __UpperCamelCase	:	Dict		    =  prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=_lowerCamelCase     ,			numpify=_lowerCamelCase    )
         for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertIsInstance(video[0]     ,			np.ndarray    )

         # Test not batched input
         __UpperCamelCase	:	Optional[Any]		    =  image_processing(video_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	Any		    =  image_processing(_lowerCamelCase     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)







     def lowerCAmelCase     (		self    ):
         # Initialize image_processing
         __UpperCamelCase	:	List[str]		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         __UpperCamelCase	:	Optional[Any]		    =  prepare_video_inputs(self.image_processor_tester     ,			equal_resolution=_lowerCamelCase     ,			torchify=_lowerCamelCase    )
         for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertIsInstance(video[0]     ,			torch.Tensor    )

         # Test not batched input
         __UpperCamelCase	:	Union[str, Any]		    =  image_processing(video_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	List[str]		    =  image_processing(_lowerCamelCase     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_videos.shape     ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )     ,			)




'''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  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[2]			)

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







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

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







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

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

    # intermediate weighs
    __UpperCamelCase	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  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	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

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

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

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


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)



