




'''simple docstring'''

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor






class 						a				:


				def __init__(		self    :					Any       ,				lowercase_    :					str       ,				lowercase_    :					str=13       ,				lowercase_    :					Dict=30       ,				lowercase_    :					Tuple=2       ,				lowercase_    :					str=3       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Dict=32       ,				lowercase_    :					List[str]=5       ,				lowercase_    :					Union[str, Any]=4       ,				lowercase_    :					Tuple=37       ,				lowercase_    :					Union[str, Any]="gelu"       ,				lowercase_    :					List[str]=0.1       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					str=10       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					str=None       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				image_size
									snake_case_									=				patch_size
									snake_case_									=				num_channels
									snake_case_									=				is_training
									snake_case_									=				use_labels
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				type_sequence_label_size
									snake_case_									=				initializer_range
									snake_case_									=				scope

									# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									snake_case_									=				(image_size // patch_size) ** 2
									snake_case_									=				num_patches + 1


				def   A_ (		self    :					str					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									snake_case_									=				None
									if self.use_labels:
														snake_case_									=				ids_tensor([self.batch_size]       ,				self.type_sequence_label_size					)

									snake_case_									=				self.get_config()

									return config, pixel_values, labels


				def   A_ (		self    :					Union[str, Any]					):
									return ViTMSNConfig(
									    image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				initializer_range=self.initializer_range       ,				)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					int       ,				lowercase_    :					Dict					):
									snake_case_									=				ViTMSNModel(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Dict       ,				lowercase_    :					Any       ,				lowercase_    :					List[Any]					):
									snake_case_									=				self.type_sequence_label_size
									snake_case_									=				ViTMSNForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				labels=lowercase_					)
									print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''					)
									print('''Labels: {labels}'''					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)					)

									# test greyscale images
									snake_case_									=				1
									snake_case_									=				ViTMSNForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()

									snake_case_									=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				self.prepare_config_and_inputs()
									snake_case_			,snake_case_			,snake_case_									=				config_and_inputs
									snake_case_									=				{'''pixel_values''': pixel_values}
									return config, inputs_dict







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					int					):
									snake_case_									=				ViTMSNModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_       ,				hidden_size=37					)


				def   A_ (		self    :					Dict					):
									self.config_tester.run_common_tests()


				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''					)
				def   A_ (		self    :					List[Any]					):
									pass


				def   A_ (		self    :					Tuple					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				model_class(lowercase_					)
														self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)					)
														snake_case_									=				model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(lowercase_       ,				nn.Linear					)					)


				def   A_ (		self    :					List[str]					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				model_class(lowercase_					)
														snake_case_									=				inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_									=				[*signature.parameters.keys()]

														snake_case_									=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,				lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_					)


				def   A_ (		self    :					int					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_					)


				@slow
				def   A_ (		self    :					Dict					):
									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_									=				ViTMSNModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)







def 		__magic_name__    (	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






@require_torch
@require_vision
class 						a				(   unittest.TestCase     ):


				@cached_property
				def   A_ (		self    :					Any					):
									return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''					) if is_vision_available() else None


				@slow
				def   A_ (		self    :					str					):
									torch.manual_seed(2					)
									snake_case_									=				ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''					).to(lowercase_					)

									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(images=lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)

									# forward pass
									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									# verify the logits
									snake_case_									=				torch.Size((1, 1000)					)
									self.assertEqual(outputs.logits.shape       ,				lowercase_					)

									snake_case_									=				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowercase_					)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowercase_       ,				atol=1e-4					)					)








"""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   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str=None							)			->      Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      __SCREAMING_SNAKE_CASE   :Dict						=  nn.Parameter(a_							)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  nn.Parameter(a_							)



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

      # lsh weights + output
      __SCREAMING_SNAKE_CASE   :List[Any]						=  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
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[2][0][1][2]

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

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

      # intermediate dense
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][0]							)
      __SCREAMING_SNAKE_CASE   :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
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][0]							)
      __SCREAMING_SNAKE_CASE   :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      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		str ,   a_     :		List[Any]							)			->      Optional[Any]:
      # reformer model
      __SCREAMING_SNAKE_CASE   :Dict						=  torch_model.reformer

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

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

      __SCREAMING_SNAKE_CASE   :Optional[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							):
            __SCREAMING_SNAKE_CASE   :Dict						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(a_ ,   a_ ,   a_							)

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

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



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Dict ,   a_     :		Dict							)			->      Tuple:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE   :List[str]						=  ReformerConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ReformerModelWithLMHead(a_							)

      with open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :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)