



import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor






class     __a			:



	def __init__(    self				:     Any					,						snake_case_				:     int					,						snake_case_				:     int=13					,						snake_case_				:     Any=30					,						snake_case_				:     Any=2					,						snake_case_				:     Tuple=3					,						snake_case_				:     Union[str, Any]=True					,						snake_case_				:     Optional[int]=True					,						snake_case_				:     List[str]=32					,						snake_case_				:     Dict=5					,						snake_case_				:     List[Any]=4					,						snake_case_				:     Optional[Any]=37					,						snake_case_				:     Dict="gelu"					,						snake_case_				:     Union[str, Any]=0.1					,						snake_case_				:     str=0.1					,						snake_case_				:     Optional[int]=10					,						snake_case_				:     Optional[int]=0.0_2					,						snake_case_				:     Union[str, Any]=3					,						snake_case_				:     List[Any]=0.6					,						snake_case_				:     Any=None					,						)->				Tuple:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =image_size
						__lowerCAmelCase						 =patch_size
						__lowerCAmelCase						 =num_channels
						__lowerCAmelCase						 =is_training
						__lowerCAmelCase						 =use_labels
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_hidden_layers
						__lowerCAmelCase						 =num_attention_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =hidden_act
						__lowerCAmelCase						 =hidden_dropout_prob
						__lowerCAmelCase						 =attention_probs_dropout_prob
						__lowerCAmelCase						 =type_sequence_label_size
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =mask_ratio
						__lowerCAmelCase						 =scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						__lowerCAmelCase						 =(image_size // patch_size) ** 2
						__lowerCAmelCase						 =int(math.ceil((1 - mask_ratio) * (num_patches + 1)))



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Optional[Any]:
						__lowerCAmelCase						 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

						__lowerCAmelCase						 =self.get_config()

						return config, pixel_values, labels



	def 		UpperCamelCase				(    self				:     Tuple)->				List[str]:
						return ViTMAEConfig(
						    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					,						is_decoder=a_					,						initializer_range=self.initializer_range					,						mask_ratio=self.mask_ratio					,						)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     str					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Any:
						__lowerCAmelCase						 =ViTMAEModel(config=a_)
						model.to(a_)
						model.eval()
						__lowerCAmelCase						 =model(a_)
						self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase				(    self				:     Union[str, Any]					,						snake_case_				:     str					,						snake_case_				:     int					,						snake_case_				:     Optional[Any])->				List[str]:
						__lowerCAmelCase						 =ViTMAEForPreTraining(a_)
						model.to(a_)
						model.eval()
						__lowerCAmelCase						 =model(a_)
						__lowerCAmelCase						 =(self.image_size // self.patch_size) ** 2
						__lowerCAmelCase						 =self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape					,						(self.batch_size, num_patches, expected_num_channels))

						# test greyscale images
						__lowerCAmelCase						 =1
						__lowerCAmelCase						 =ViTMAEForPreTraining(a_)
						model.to(a_)
						model.eval()
						__lowerCAmelCase						 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
						__lowerCAmelCase						 =model(a_)
						__lowerCAmelCase						 =self.patch_size**2
						self.parent.assertEqual(result.logits.shape					,						(self.batch_size, num_patches, expected_num_channels))



	def 		UpperCamelCase				(    self				:     Dict)->				Tuple:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"pixel_values": pixel_values}
						return config, inputs_dict






@require_torch
class     __a			(  _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE  	=							{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Dict)->				int:
						__lowerCAmelCase						 =ViTMAEModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=a_					,						has_text_modality=a_					,						hidden_size=37)



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				int:
						self.config_tester.run_common_tests()



	@unittest.skip(reason="""ViTMAE does not use inputs_embeds""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[int]:
						pass



	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

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



	def 		UpperCamelCase				(    self				:     Any)->				int:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCAmelCase						 =["pixel_values"]
											self.assertListEqual(arg_names[:1]					,						a_)



	def 		UpperCamelCase				(    self				:     Any)->				Optional[int]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*a_)



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     Any					,						snake_case_				:     str					,						snake_case_				:     Any)->				List[Any]:
						np.random.seed(2)

						__lowerCAmelCase						 =int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
						__lowerCAmelCase						 =np.random.uniform(size=(self.model_tester.batch_size, num_patches))
						__lowerCAmelCase						 =torch.from_numpy(a_)

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						__lowerCAmelCase						 =pt_noise

						super().check_pt_tf_models(a_					,						a_					,						a_)



	def 		UpperCamelCase				(    self				:     Dict)->				Union[str, Any]:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(a_)
											model.to(a_)
											model.eval()
											# make random mask reproducible
											torch.manual_seed(2)
											with torch.no_grad():
																__lowerCAmelCase						 =model(**self._prepare_for_class(a_					,						a_))

											__lowerCAmelCase						 =outputs[0].cpu().numpy()
											__lowerCAmelCase						 =0

											with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(a_)
																__lowerCAmelCase						 =model_class.from_pretrained(a_)
																model.to(a_)
																# make random mask reproducible
																torch.manual_seed(2)
																with torch.no_grad():
																					__lowerCAmelCase						 =model(**self._prepare_for_class(a_					,						a_))

																# Make sure we don't have nans
																__lowerCAmelCase						 =after_outputs[0].cpu().numpy()
																__lowerCAmelCase						 =0
																__lowerCAmelCase						 =np.amax(np.abs(out_a - out_a))
																self.assertLessEqual(a_					,						1e-5)



	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						pass



	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.""")
	def 		UpperCamelCase				(    self				:     Any)->				Any:
						pass



	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.""")
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Optional[int]:
						pass



	@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[Any]:
						pass



	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
	def 		UpperCamelCase				(    self				:     Tuple)->				Tuple:
						pass



	@slow
	def 		UpperCamelCase				(    self				:     str)->				Any:



						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase						 =ViTMAEModel.from_pretrained(a_)
											self.assertIsNotNone(a_)






def        __lowerCAmelCase		(	)	->    Optional[int]:
					__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
					return image






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



	@cached_property
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""") if is_vision_available() else None



	@slow
	def 		UpperCamelCase				(    self				:     Tuple)->				Union[str, Any]:
						np.random.seed(2)

						__lowerCAmelCase						 =ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""").to(a_)

						__lowerCAmelCase						 =self.default_image_processor
						__lowerCAmelCase						 =prepare_img()
						__lowerCAmelCase						 =image_processor(images=a_					,						return_tensors="""pt""").to(a_)

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						__lowerCAmelCase						 =ViTMAEConfig()
						__lowerCAmelCase						 =int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
						__lowerCAmelCase						 =np.random.uniform(size=(1, num_patches))

						# forward pass
						with torch.no_grad():
											__lowerCAmelCase						 =model(**a_					,						noise=torch.from_numpy(a_).to(device=a_))

						# verify the logits
						__lowerCAmelCase						 =torch.Size((1, 1_96, 7_68))
						self.assertEqual(outputs.logits.shape					,						a_)

						__lowerCAmelCase						 =torch.tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]])

						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,						expected_slice.to(a_)					,						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      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			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      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	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      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# lsh weights + output
			a__  :	Dict		 =			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
			a__  :	Optional[int]		 =			weights[2][0][1][2]

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

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

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, 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
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	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      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

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

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	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"
									a__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

			a__  :	Optional[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			):
						a__  :	Union[str, Any]		 =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

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

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






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	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__":
		__UpperCAmelCase						=			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.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)