





'''simple docstring'''




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
			import torch
			from torch import nn

			from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
			from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DPTImageProcessor






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=2      ,       UpperCamelCase_=32      ,       UpperCamelCase_=16      ,       UpperCamelCase_=3      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=32      ,       UpperCamelCase_=4      ,       UpperCamelCase_=[0, 1, 2, 3]      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=[1, 384, 24, 24]      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       )      ->		Any:
		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__	       =   backbone_out_indices
		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__	       =   initializer_range
		snake_case__	       =   num_labels
		snake_case__	       =   backbone_featmap_shape
		snake_case__	       =   scope
		snake_case__	       =   is_hybrid
		# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
		snake_case__	       =   (image_size // patch_size) ** 2
		snake_case__	       =   num_patches + 1





	def   _snake_case   (		self		)      ->		int:
		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.image_size, self.image_size]      ,       self.num_labels		)

		snake_case__	       =   self.get_config()

		return config, pixel_values, labels





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   {
		    'global_padding': 'same',
		    'layer_type': 'bottleneck',
		    'depths': [3, 4, 9],
		    'out_features': ['stage1', 'stage2', 'stage3'],
		    'embedding_dynamic_padding': True,
		    'hidden_sizes': [96, 192, 384, 768],
		    'num_groups': 2,
		}

		return DPTConfig(
		    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      ,       backbone_out_indices=self.backbone_out_indices      ,       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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       is_hybrid=self.is_hybrid      ,       backbone_config=UpperCamelCase_      ,       backbone_featmap_shape=self.backbone_featmap_shape      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   DPTModel(config=UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		str:
		snake_case__	       =   self.num_labels
		snake_case__	       =   DPTForDepthEstimation(UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(result.predicted_depth.shape      ,       (self.batch_size, self.image_size, self.image_size)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   self.num_labels
		snake_case__	       =   DPTForSemanticSegmentation(UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_      ,       labels=UpperCamelCase_		)
		self.parent.assertEqual(
		    result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		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 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	__lowerCAmelCase       						=  (
	    {
	        '''depth-estimation''': DPTForDepthEstimation,
	        '''feature-extraction''': DPTModel,
	        '''image-segmentation''': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   DPTModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       has_text_modality=UpperCamelCase_      ,       hidden_size=37		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		self.config_tester.run_common_tests()





	@unittest.skip(reason='DPT does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		Any:
		pass





	def   _snake_case   (		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(UpperCamelCase_		)
			self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)		)
			snake_case__	       =   model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(UpperCamelCase_      ,       nn.Linear		)		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		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(UpperCamelCase_		)
			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]      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()
			snake_case__	       =   True

			if model_class in get_values(UpperCamelCase_		):
				continue

			snake_case__	       =   model_class(UpperCamelCase_		)
			model.to(UpperCamelCase_		)
			model.train()
			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_      ,       return_labels=UpperCamelCase_		)
			snake_case__	       =   model(**UpperCamelCase_		).loss
			loss.backward()





	def   _snake_case   (		self		)      ->		List[str]:
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()
			snake_case__	       =   False
			snake_case__	       =   True

			if model_class in get_values(UpperCamelCase_		) or not model_class.supports_gradient_checkpointing:
				continue
			snake_case__	       =   model_class(UpperCamelCase_		)
			model.to(UpperCamelCase_		)
			model.gradient_checkpointing_enable()
			model.train()
			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_      ,       return_labels=UpperCamelCase_		)
			snake_case__	       =   model(**UpperCamelCase_		).loss
			loss.backward()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   _config_zero_init(UpperCamelCase_		)
		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(config=UpperCamelCase_		)
			# Skip the check for the backbone
			snake_case__	       =   []
			for name, module in model.named_modules():
				if module.__class__.__name__ == "DPTViTHybridEmbeddings":
					snake_case__	       =   [F'''{name}.{key}''' for key in module.state_dict().keys()]
					break

			for name, param in model.named_parameters():
				if param.requires_grad:
					if name in backbone_params:
						continue
					self.assertIn(
					    ((param.data.mean() * 1E9).round() / 1E9).item()      ,       [0.0, 1.0]      ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,       )





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





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
			snake_case__	       =   DPTModel.from_pretrained(UpperCamelCase_		)
			self.assertIsNotNone(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()
		snake_case__	       =   'add'
		with self.assertRaises(UpperCamelCase_		):
			snake_case__	       =   DPTForDepthEstimation(UpperCamelCase_		)





def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







@require_torch
@require_vision
@slow
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
		snake_case__	       =   DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(UpperCamelCase_		)

		snake_case__	       =   prepare_img()
		snake_case__	       =   image_processor(images=UpperCamelCase_      ,       return_tensors='pt'		).to(UpperCamelCase_		)

		# forward pass
		with torch.no_grad():
			snake_case__	       =   model(**UpperCamelCase_		)
			snake_case__	       =   outputs.predicted_depth

		# verify the predicted depth
		snake_case__	       =   torch.Size((1, 384, 384)		)
		self.assertEqual(predicted_depth.shape      ,       UpperCamelCase_		)

		snake_case__	       =   torch.tensor(
		    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]		).to(UpperCamelCase_		)

		self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100      ,       UpperCamelCase_      ,       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			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   np.asarray(weights[3]							)

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

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

	# intermediate weighs
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

	# layernorm 2
	snake_case__	       =   np.asarray(intermediate_weights[0][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[0][1]							)
	set_param(
	    torch_block.feed_forward.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[1][1]							)
	set_param(
	    torch_block.feed_forward.dense.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate out
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[4][1]							)
	set_param(
	    torch_block.feed_forward.output.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

	# word embeds
	snake_case__	       =   np.asarray(weights[1]							)
	set_param(
	    torch_model_reformer.embeddings.word_embeddings					,				torch.tensor(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   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'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

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

	# output layer norm
	snake_case__	       =   np.asarray(weights[7][0]							)
	snake_case__	       =   np.asarray(weights[7][1]							)
	set_param(
	    torch_model_reformer.encoder.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	snake_case__	       =   np.asarray(weights[9][1]							)
	set_param(
	    torch_model.lm_head.decoder					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




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

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   pickle.load(UpperCAmelCase_							)['weights']

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

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


if __name__ == "__main__":
			a__					:			Optional[int]          =     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__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



