


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


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str  ):
 # set torch weights for 1-to-1 comparison
 lowercase__       :       int				=						np.asarray(weights[0]  )
 lowercase__       :       Dict				=						np.asarray(weights[1]  )
 lowercase__       :       int				=						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__	:							Optional[Any]       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[int]  ):
 # set torch weights for 1-to-1 comparison
 lowercase__       :       str				=						np.asarray(weights[0]  )
 lowercase__       :       Any				=						np.asarray(weights[1]  )
 lowercase__       :       Tuple				=						np.asarray(weights[2]  )
 lowercase__       :       int				=						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__	:							List[str]       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[int]  ):
 # layernorm 1
 lowercase__       :       Optional[int]				=						weights[0][0][0]
 lowercase__       :       Union[str, Any]				=						np.asarray(layer_norm_a[0]  )
 lowercase__       :       Optional[int]				=						np.asarray(layer_norm_a[1]  )
 set_param(
     torch_block.attention.layer_norm       ,		torch.tensor(lowerCamelCase__  )       ,		torch.tensor(lowerCamelCase__  )       ,		)

 # lsh weights + output
 lowercase__       :       int				=						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
 lowercase__       :       Union[str, Any]				=						weights[2][0][1][2]

 # Chunked Feed Forward
 if len(lowerCamelCase__  ) == 4:
  lowercase__       :       str				=						intermediate_weights[2]

 # layernorm 2
 lowercase__       :       Union[str, Any]				=						np.asarray(intermediate_weights[0][0]  )
 lowercase__       :       int				=						np.asarray(intermediate_weights[0][1]  )
 set_param(
     torch_block.feed_forward.layer_norm       ,		torch.tensor(lowerCamelCase__  )       ,		torch.tensor(lowerCamelCase__  )       ,		)

 # intermediate dense
 lowercase__       :       Optional[Any]				=						np.asarray(intermediate_weights[1][0]  )
 lowercase__       :       Any				=						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
 lowercase__       :       Dict				=						np.asarray(intermediate_weights[4][0]  )
 lowercase__       :       Dict				=						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__	:							List[Any]       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							List[Any]  ):
 # reformer model
 lowercase__       :       List[str]				=						torch_model.reformer

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

 if isinstance(weights[3]       ,		lowerCamelCase__  ):
  lowercase__       :       Dict				=						torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights  )  ):
   lowercase__       :       Optional[int]				=						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'''
   lowercase__       :       List[Any]				=						nn.Parameter(torch.tensor(lowerCamelCase__  )  )

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

 # output layer norm
 lowercase__       :       Union[str, Any]				=						np.asarray(weights[7][0]  )
 lowercase__       :       Union[str, Any]				=						np.asarray(weights[7][1]  )
 set_param(
     torch_model_reformer.encoder.layer_norm       ,		torch.tensor(lowerCamelCase__  )       ,		torch.tensor(lowerCamelCase__  )       ,		)

 # output embeddings
 lowercase__       :       Tuple				=						np.asarray(weights[9][0]  )
 lowercase__       :       List[Any]				=						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__	:							Optional[Any]       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							str  ):
 # Initialise PyTorch model
 lowercase__       :       str				=						ReformerConfig.from_json_file(lowerCamelCase__  )
 print(f'''Building PyTorch model from configuration: {config}'''  )
 lowercase__       :       List[str]				=						ReformerModelWithLMHead(lowerCamelCase__  )

 with open(lowerCamelCase__       ,		"""rb"""  ) as f:
  lowercase__       :       List[str]				=						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__":
   __snake_case    					=					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.'
   )
   __snake_case    					=					parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)