
'''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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def __init__(		self      :     Union[str, Any]							,_a      :     int							,_a      :     List[Any]=7							,_a      :     Any=3							,_a      :     Optional[Any]=18							,_a      :     Any=30							,_a      :     List[str]=400							,_a      :     Dict=True							,_a      :     int=None							,_a      :     Optional[Any]=True							,_a      :     str=None							,_a      :     Optional[int]=True							,_a      :     Any=[0.4814_5466, 0.457_8275, 0.4082_1073]							,_a      :     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,_a      :     Optional[int]=True							,):


     '''simple docstring'''






     _a				:	Tuple			=   size if size is not None else {'height': 224, 'width': 224}
     _a				:	Any			=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
     _a				:	Optional[Any]			=   parent
     _a				:	List[str]			=   batch_size
     _a				:	Union[str, Any]			=   num_channels
     _a				:	str			=   image_size
     _a				:	Dict			=   min_resolution
     _a				:	Union[str, Any]			=   max_resolution
     _a				:	List[Any]			=   do_resize
     _a				:	Optional[int]			=   size
     _a				:	int			=   do_center_crop
     _a				:	Any			=   crop_size
     _a				:	Optional[int]			=   do_normalize
     _a				:	Union[str, Any]			=   image_mean
     _a				:	Union[str, Any]			=   image_std
     _a				:	Any			=   do_convert_rgb


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Optional[Any]=False							,_a      :     Union[str, Any]=False							,_a      :     int=False       ):


     '''simple docstring'''






     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
        _a				:	Dict			=   []
        for i in range(self.batch_size       ):
           image_inputs.append(
               np.random.randint(
                   255							,size=(self.num_channels, self.max_resolution, self.max_resolution)							,dtype=np.uinta       )       )
     else:
        _a				:	int			=   []
        for i in range(self.batch_size       ):
           _a,  _a				:	Union[str, Any]			=   np.random.choice(np.arange(self.min_resolution							,self.max_resolution       )							,2       )
           image_inputs.append(np.random.randint(255							,size=(self.num_channels, width, height)							,dtype=np.uinta       )       )

     if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        _a				:	List[Any]			=   [Image.fromarray(np.moveaxis(_a							,0							,-1       )       ) for x in image_inputs]

     if torchify:
        _a				:	Any			=   [torch.from_numpy(_a       ) for x in image_inputs]

     return image_inputs







@require_torch
@require_vision
class UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					ChineseCLIPImageProcessor if is_vision_available() else None


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   ChineseCLIPImageProcessingTester(self							,do_center_crop=_a       )


  @property
  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     return self.image_processor_tester.prepare_image_processor_dict()


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(_a							,'do_resize'       )       )
     self.assertTrue(hasattr(_a							,'size'       )       )
     self.assertTrue(hasattr(_a							,'do_center_crop'       )       )
     self.assertTrue(hasattr(_a							,'center_crop'       )       )
     self.assertTrue(hasattr(_a							,'do_normalize'       )       )
     self.assertTrue(hasattr(_a							,'image_mean'       )       )
     self.assertTrue(hasattr(_a							,'image_std'       )       )
     self.assertTrue(hasattr(_a							,'do_convert_rgb'       )       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size							,{'height': 224, 'width': 224}       )
     self.assertEqual(image_processor.crop_size							,{'height': 18, 'width': 18}       )

     _a				:	List[Any]			=   self.image_processing_class.from_dict(self.image_processor_dict							,size=42							,crop_size=84       )
     self.assertEqual(image_processor.size							,{'shortest_edge': 42}       )
     self.assertEqual(image_processor.crop_size							,{'height': 84, 'width': 84}       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     _a				:	Dict			=   self.image_processor_tester.prepare_inputs(equal_resolution=_a       )
     for image in image_inputs:
        self.assertIsInstance(_a							,Image.Image       )

     # Test not batched input
     _a				:	Optional[int]			=   image_processing(image_inputs[0]							,return_tensors='pt'       ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )							,)

     # Test batched
     _a				:	str			=   image_processing(_a							,return_tensors='pt'       ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )							,)


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     _a				:	Optional[int]			=   self.image_processor_tester.prepare_inputs(equal_resolution=_a							,numpify=_a       )
     for image in image_inputs:
        self.assertIsInstance(_a							,np.ndarray       )

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

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


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     _a				:	int			=   self.image_processor_tester.prepare_inputs(equal_resolution=_a							,torchify=_a       )
     for image in image_inputs:
        self.assertIsInstance(_a							,torch.Tensor       )

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

     # Test batched
     _a				:	Dict			=   image_processing(_a							,return_tensors='pt'       ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )							,)







@require_torch
@require_vision
class UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				List[Any]   =					ChineseCLIPImageProcessor if is_vision_available() else None


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   ChineseCLIPImageProcessingTester(self							,num_channels=4							,do_center_crop=_a       )
     _a				:	Union[str, Any]			=   3


  @property
  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.image_processor_tester.prepare_image_processor_dict()


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(_a							,'do_resize'       )       )
     self.assertTrue(hasattr(_a							,'size'       )       )
     self.assertTrue(hasattr(_a							,'do_center_crop'       )       )
     self.assertTrue(hasattr(_a							,'center_crop'       )       )
     self.assertTrue(hasattr(_a							,'do_normalize'       )       )
     self.assertTrue(hasattr(_a							,'image_mean'       )       )
     self.assertTrue(hasattr(_a							,'image_std'       )       )
     self.assertTrue(hasattr(_a							,'do_convert_rgb'       )       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     _a				:	List[str]			=   self.image_processor_tester.prepare_inputs(equal_resolution=_a       )
     for image in image_inputs:
        self.assertIsInstance(_a							,Image.Image       )

     # Test not batched input
     _a				:	Union[str, Any]			=   image_processing(image_inputs[0]							,return_tensors='pt'       ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )							,)

     # Test batched
     _a				:	Union[str, Any]			=   image_processing(_a							,return_tensors='pt'       ).pixel_values
     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_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      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   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      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	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      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

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

   # lsh weights + output
   _a				:	List[str]			=   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
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   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
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   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      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

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

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   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				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   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 ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

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

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   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      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

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

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   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__":
  __lowerCAmelCase         =    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         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



