



'''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 	__magic_name__						:







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







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Dict        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Dict        =							None
									if self.use_labels:
											_a     :      Optional[int]        =							ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels		)

									_a     :      Any        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Union[str, Any]        =							{
									    '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 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Tuple        =							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Optional[int]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Union[str, Any]		):
									_a     :      Dict        =							self.num_labels
									_a     :      List[Any]        =							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Union[str, Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str		):
									_a     :      Union[str, Any]        =							self.num_labels
									_a     :      Union[str, Any]        =							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Tuple        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Dict        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Any        =							config_and_inputs
									_a     :      Optional[Any]        =							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    List[Any]   				=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase       :    Tuple   				=					(
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Any   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Union[str, Any]        =							DPTModelTester(self		)
									_a     :      List[Any]        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







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







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







							def 							__lowercase			(							self					:       str		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      str        =							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()	,(nn.Module)		)
											_a     :      List[Any]        =							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase	,nn.Linear		)		)







							def 							__lowercase			(							self					:       Dict		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      Optional[Any]        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a       ,					_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
											_a     :      Optional[Any]        =							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a     :      List[Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a     :      int        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      str        =							model(**_UpperCAmelCase		).loss
											loss.backward()







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

											_a       ,					_a     :      Tuple        =							self.model_tester.prepare_config_and_inputs_for_common()
											_a     :      Optional[int]        =							False
											_a     :      Optional[int]        =							True

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a     :      Any        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a     :      Optional[Any]        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      str        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

									_a     :      List[str]        =							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a     :      Optional[Any]        =							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a     :      Optional[Any]        =							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a     :      int        =							[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 							__lowercase			(							self					:       Tuple		):
									pass







							@slow
							def 							__lowercase			(							self					:       Tuple		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a     :      int        =							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()
									_a     :      int        =							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a     :      Dict        =							DPTForDepthEstimation(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      List[Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							def 							__lowercase			(							self					:       str		):
									_a     :      Optional[int]        =							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a     :      List[Any]        =							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a     :      Optional[int]        =							prepare_img()
									_a     :      str        =							image_processor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      int        =							model(**_UpperCAmelCase		)
											_a     :      Union[str, Any]        =							outputs.predicted_depth

									# verify the predicted depth
									_a     :      List[str]        =							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							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(_UpperCAmelCase		)

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









'''simple docstring'''

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.array:
		_a     :      Optional[int]        =							f"""{sampling_rate}"""
		_a     :      Any        =							'1'
		_a     :      Optional[int]        =							'f32le'
		_a     :      Any        =							[
		    'ffmpeg',
		    '-i',
		    'pipe:0',
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]

		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdin=subprocess.PIPE						,			stdout=subprocess.PIPE  ) as ffmpeg_process:
						_a     :      int        =							ffmpeg_process.communicate(lowerCAmelCase_  )
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
		_a     :      int        =							output_stream[0]
		_a     :      List[str]        =							np.frombuffer(lowerCAmelCase_						,			np.floataa  )
		if audio.shape[0] == 0:
				raise ValueError('Malformed soundfile'  )
		return audio



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = "f32le"						,			)			->      Union[str, Any]:
		_a     :      List[str]        =							f"""{sampling_rate}"""
		_a     :      List[str]        =							'1'
		if format_for_conversion == "s16le":
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Dict        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		_a     :      Any        =							platform.system()
		if system == "Linux":
				_a     :      Union[str, Any]        =							'alsa'
				_a     :      Union[str, Any]        =							'default'
		elif system == "Darwin":
				_a     :      Any        =							'avfoundation'
				_a     :      Optional[int]        =							':0'
		elif system == "Windows":
				_a     :      str        =							'dshow'
				_a     :      Tuple        =							'default'

		_a     :      str        =							[
		    'ffmpeg',
		    '-f',
		    format_,
		    '-i',
		    input_,
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-fflags',
		    'nobuffer',
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		_a     :      Union[str, Any]        =							_ffmpeg_stream(lowerCAmelCase_						,			lowerCAmelCase_  )
		for item in iterator:
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = "f32le"						,			)			->      str:
		if stream_chunk_s is not None:
				_a     :      str        =							stream_chunk_s
		else:
				_a     :      List[str]        =							chunk_length_s

		_a     :      int        =							ffmpeg_microphone(lowerCAmelCase_						,			lowerCAmelCase_						,			format_for_conversion=lowerCAmelCase_  )
		if format_for_conversion == "s16le":
				_a     :      Optional[Any]        =							np.intaa
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Tuple        =							np.floataa
				_a     :      Any        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		if stride_length_s is None:
				_a     :      str        =							chunk_length_s / 6
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		if isinstance(lowerCAmelCase_						,			(int, float)  ):
				_a     :      List[str]        =							[stride_length_s, stride_length_s]

		_a     :      str        =							int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
		_a     :      List[str]        =							int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
		_a     :      Any        =							datetime.datetime.now()
		_a     :      Dict        =							datetime.timedelta(seconds=lowerCAmelCase_  )
		for item in chunk_bytes_iter(lowerCAmelCase_						,			lowerCAmelCase_						,			stride=(stride_left, stride_right)						,			stream=lowerCAmelCase_  ):
				# Put everything back in numpy scale
				_a     :      List[Any]        =							np.frombuffer(item['raw']						,			dtype=lowerCAmelCase_  )
				_a     :      List[str]        =							(
				    item['stride'][0] // size_of_sample,
				    item['stride'][1] // size_of_sample,
				)
				_a     :      Union[str, Any]        =							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      Tuple        =							B''
		_a       ,					_a     :      str        =							stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""  )
		_a     :      Optional[int]        =							0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase_  ) < chunk_len:
						_a     :      str        =							(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase_  ) >= chunk_len:
								# We are flushing the accumulator
								_a     :      Union[str, Any]        =							(_stride_left, stride_right)
								_a     :      Dict        =							{'raw': acc[:chunk_len], 'stride': stride}
								if stream:
										_a     :      List[str]        =							False
								yield item
								_a     :      int        =							stride_left
								_a     :      List[Any]        =							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase_  ) > stride_left:
				_a     :      str        =							{'raw': acc, 'stride': (_stride_left, 0)}
				if stream:
						_a     :      str        =							False
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      Optional[Any]        =							2**24  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdout=subprocess.PIPE						,			bufsize=lowerCAmelCase_  ) as ffmpeg_process:
						while True:
								_a     :      Any        =							ffmpeg_process.stdout.read(lowerCAmelCase_  )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error




