
"""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 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[str]	,A_  :   Tuple	,A_  :   List[str]=2	,A_  :   Optional[Any]=32	,A_  :   Union[str, Any]=16	,A_  :   Dict=3	,A_  :   Optional[Any]=True	,A_  :   Optional[int]=True	,A_  :   Optional[int]=32	,A_  :   List[Any]=4	,A_  :   Dict=[0, 1, 2, 3]	,A_  :   str=4	,A_  :   List[Any]=37	,A_  :   Dict="gelu"	,A_  :   Optional[Any]=0.1	,A_  :   Tuple=0.1	,A_  :   List[Any]=0.02	,A_  :   List[str]=3	,A_  :   Tuple=[1, 384, 24, 24]	,A_  :   List[str]=True	,A_  :   Union[str, Any]=None	,)  -> Any:
							A     = parent
							A     = batch_size
							A     = image_size
							A     = patch_size
							A     = num_channels
							A     = is_training
							A     = use_labels
							A     = hidden_size
							A     = num_hidden_layers
							A     = backbone_out_indices
							A     = num_attention_heads
							A     = intermediate_size
							A     = hidden_act
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = initializer_range
							A     = num_labels
							A     = backbone_featmap_shape
							A     = scope
							A     = is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							A     = (image_size // patch_size) ** 2
							A     = num_patches + 1


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							A     = None
							if self.use_labels:
								A     = ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels   )

							A     = self.get_config()

							return config, pixel_values, labels


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = {
							    '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=A_	,initializer_range=self.initializer_range	,is_hybrid=self.is_hybrid	,backbone_config=A_	,backbone_featmap_shape=self.backbone_featmap_shape	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Dict	,A_  :   Optional[Any]	,A_  :   Dict   )  -> List[str]:
							A     = DPTModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Any	,A_  :   int	,A_  :   Tuple   )  -> Dict:
							A     = self.num_labels
							A     = DPTForDepthEstimation(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )
							self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[Any]	,A_  :   str	,A_  :   int   )  -> List[str]:
							A     = self.num_labels
							A     = DPTForSemanticSegmentation(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,labels=A_   )
							self.parent.assertEqual(
							    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> int:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A     = config_and_inputs
							A     = {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Union[str, Any]   =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						_lowerCamelCase:     Optional[Any]   =     (
						    {
						        '''depth-estimation''': DPTForDepthEstimation,
						        '''feature-extraction''': DPTModel,
						        '''image-segmentation''': DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowerCamelCase:     Union[str, Any]   =     False
						_lowerCamelCase:     Dict   =     False
						_lowerCamelCase:     str   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> List[str]:
							A     = DPTModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,has_text_modality=A_	,hidden_size=37   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Any:
							self.config_tester.run_common_tests()


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[str]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

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

								A     = ['pixel_values']
								self.assertListEqual(arg_names[:1]	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> int:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
									continue

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

								if model_class in get_values(A_   ):
									continue

								A     = model_class(A_   )
								model.to(A_   )
								model.train()
								A     = self._prepare_for_class(A_	,A_	,return_labels=A_   )
								A     = model(**A_   ).loss
								loss.backward()


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

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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							A     = _config_zero_init(A_   )
							for model_class in self.all_model_classes:
								A     = model_class(config=A_   )
								# Skip the check for the backbone
								A     = []
								for name, module in model.named_modules():
									if module.__class__.__name__ == "DPTViTHybridEmbeddings":
										A     = [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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Tuple:
							pass


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> str:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
								A     = DPTModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[Any]:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 'add'
							with self.assertRaises(A_   ):
								A     = DPTForDepthEstimation(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







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



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Dict:
							A     = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
							A     = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(A_   )

							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='pt'   ).to(A_   )

							# forward pass
							with torch.no_grad():
								A     = model(**A_   )
								A     = outputs.predicted_depth

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

							A     = torch.tensor(
							    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]   ).to(A_   )

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100	,A_	,atol=1e-4   )   )

"""simple docstring"""


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase				 =					'''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''






def    _snake_case					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

	parser.add_argument(
	    '--config_file'						,   default=snake_case__						,   help=(
	        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
	        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
	        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
	        'with \'huggingface\'.'
	    )						,   )

	if subparsers is not None:
		parser.set_defaults(func=snake_case__  )
	return parser






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

	if config_file.endswith('.json'  ):
		config.to_json_file(snake_case__  )
	else:
		config.to_yaml_file(snake_case__  )
	print(F'accelerate configuration saved at {config_file}'  )






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()