



import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ConditionalDetrImageProcessor



class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=7    ,	lowerCAmelCase=3    ,	lowerCAmelCase=30    ,	lowerCAmelCase=400    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase=[0.5, 0.5, 0.5]    ,	lowerCAmelCase=[0.5, 0.5, 0.5]    ,	lowerCAmelCase=True    ,	lowerCAmelCase=1 / 255    ,	lowerCAmelCase=True    ,	):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				min_resolution
							UpperCAmelCase_						=				max_resolution
							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							UpperCAmelCase_						=				do_normalize
							UpperCAmelCase_						=				image_mean
							UpperCAmelCase_						=				image_std
							UpperCAmelCase_						=				do_rescale
							UpperCAmelCase_						=				rescale_factor
							UpperCAmelCase_						=				do_pad



					def    A__		(			self     ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=False     ):
							if not batched:
									UpperCAmelCase_						=				image_inputs[0]
									if isinstance(lowerCAmelCase    ,	Image.Image     ):
											UpperCAmelCase_					,						UpperCAmelCase_						=				image.size
									else:
											UpperCAmelCase_					,						UpperCAmelCase_						=				image.shape[1], image.shape[2]
									if w < h:
											UpperCAmelCase_						=				int(self.size["shortest_edge"] * h / w     )
											UpperCAmelCase_						=				self.size["shortest_edge"]
									elif w > h:
											UpperCAmelCase_						=				self.size["shortest_edge"]
											UpperCAmelCase_						=				int(self.size["shortest_edge"] * w / h     )
									else:
											UpperCAmelCase_						=				self.size["shortest_edge"]
											UpperCAmelCase_						=				self.size["shortest_edge"]

							else:
									UpperCAmelCase_						=				[]
									for image in image_inputs:
											UpperCAmelCase_					,						UpperCAmelCase_						=				self.get_expected_values([image]     )
											expected_values.append((expected_height, expected_width)     )
									UpperCAmelCase_						=				max(lowerCAmelCase    ,	key=lambda lowerCAmelCase     : item[0]     )[0]
									UpperCAmelCase_						=				max(lowerCAmelCase    ,	key=lambda lowerCAmelCase     : item[1]     )[1]

							return expected_height, expected_width





@require_torch
@require_vision
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[Any]									=  ConditionalDetrImageProcessor if is_vision_available() else None



					def    A__		(			self     ):
							UpperCAmelCase_						=				ConditionalDetrImageProcessingTester(self     )



					@property
					def    A__		(			self     ):
							return self.image_processor_tester.prepare_image_processor_dict()



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(lowerCAmelCase    ,	"image_mean"     )     )
							self.assertTrue(hasattr(lowerCAmelCase    ,	"image_std"     )     )
							self.assertTrue(hasattr(lowerCAmelCase    ,	"do_normalize"     )     )
							self.assertTrue(hasattr(lowerCAmelCase    ,	"do_resize"     )     )
							self.assertTrue(hasattr(lowerCAmelCase    ,	"size"     )     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.image_processing_class.from_dict(self.image_processor_dict     )
							self.assertEqual(image_processor.size    ,	{"shortest_edge": 18, "longest_edge": 1333}     )
							self.assertEqual(image_processor.do_pad    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				self.image_processing_class.from_dict(
							    self.image_processor_dict    ,	size=42    ,	max_size=84    ,	pad_and_return_pixel_mask=lowerCAmelCase     )
							self.assertEqual(image_processor.size    ,	{"shortest_edge": 42, "longest_edge": 84}     )
							self.assertEqual(image_processor.do_pad    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random PIL images
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	Image.Image     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random numpy tensors
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase    ,	numpify=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	np.ndarray     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					def    A__		(			self     ):
							# Initialize image_processing
							UpperCAmelCase_						=				self.image_processing_class(**self.image_processor_dict     )
							# create random PyTorch tensors
							UpperCAmelCase_						=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=lowerCAmelCase    ,	torchify=lowerCAmelCase     )
							for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase    ,	torch.Tensor     )

							# Test not batched input
							UpperCAmelCase_						=				image_processing(image_inputs[0]    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

							# Test batched
							UpperCAmelCase_						=				image_processing(lowerCAmelCase    ,	return_tensors="pt"     ).pixel_values

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.image_processor_tester.get_expected_values(lowerCAmelCase    ,	batched=lowerCAmelCase     )

							self.assertEqual(
							    encoded_images.shape    ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,	)



					@slow
					def    A__		(			self     ):
							# prepare image and target
							UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,	"r"     ) as f:
									UpperCAmelCase_						=				json.loads(f.read()     )

							UpperCAmelCase_						=				{"image_id": 3_9769, "annotations": target}

							# encode them
							UpperCAmelCase_						=				ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"     )
							UpperCAmelCase_						=				image_processing(images=lowerCAmelCase    ,	annotations=lowerCAmelCase    ,	return_tensors="pt"     )

							# verify pixel values
							UpperCAmelCase_						=				torch.Size([1, 3, 800, 1066]     )
							self.assertEqual(encoding["pixel_values"].shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

							# verify area
							UpperCAmelCase_						=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	lowerCAmelCase     )     )
							# verify boxes
							UpperCAmelCase_						=				torch.Size([6, 4]     )
							self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	lowerCAmelCase    ,	atol=1e-3     )     )
							# verify image_id
							UpperCAmelCase_						=				torch.tensor([3_9769]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	lowerCAmelCase     )     )
							# verify is_crowd
							UpperCAmelCase_						=				torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	lowerCAmelCase     )     )
							# verify class_labels
							UpperCAmelCase_						=				torch.tensor([75, 75, 63, 65, 17, 17]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	lowerCAmelCase     )     )
							# verify orig_size
							UpperCAmelCase_						=				torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	lowerCAmelCase     )     )
							# verify size
							UpperCAmelCase_						=				torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	lowerCAmelCase     )     )



					@slow
					def    A__		(			self     ):
							# prepare image, target and masks_path
							UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,	"r"     ) as f:
									UpperCAmelCase_						=				json.loads(f.read()     )

							UpperCAmelCase_						=				{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

							UpperCAmelCase_						=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

							# encode them
							UpperCAmelCase_						=				ConditionalDetrImageProcessor(format="coco_panoptic"     )
							UpperCAmelCase_						=				image_processing(images=lowerCAmelCase    ,	annotations=lowerCAmelCase    ,	masks_path=lowerCAmelCase    ,	return_tensors="pt"     )

							# verify pixel values
							UpperCAmelCase_						=				torch.Size([1, 3, 800, 1066]     )
							self.assertEqual(encoding["pixel_values"].shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([0.2796, 0.3138, 0.3481]     )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

							# verify area
							UpperCAmelCase_						=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	lowerCAmelCase     )     )
							# verify boxes
							UpperCAmelCase_						=				torch.Size([6, 4]     )
							self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	lowerCAmelCase    ,	atol=1e-3     )     )
							# verify image_id
							UpperCAmelCase_						=				torch.tensor([3_9769]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	lowerCAmelCase     )     )
							# verify is_crowd
							UpperCAmelCase_						=				torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	lowerCAmelCase     )     )
							# verify class_labels
							UpperCAmelCase_						=				torch.tensor([17, 17, 63, 75, 75, 93]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	lowerCAmelCase     )     )
							# verify masks
							UpperCAmelCase_						=				82_2873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,	lowerCAmelCase     )
							# verify orig_size
							UpperCAmelCase_						=				torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	lowerCAmelCase     )     )
							# verify size
							UpperCAmelCase_						=				torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	lowerCAmelCase     )     )






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

import numpy as np







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							np.array:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		UpperCAmelCase_						=				"f32le"
		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							Dict:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		UpperCAmelCase_						=				platform.system()
		if system == "Linux":
				UpperCAmelCase_						=				"alsa"
				UpperCAmelCase_						=				"default"
		elif system == "Darwin":
				UpperCAmelCase_						=				"avfoundation"
				UpperCAmelCase_						=				":0"
		elif system == "Windows":
				UpperCAmelCase_						=				"dshow"
				UpperCAmelCase_						=				"default"

		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-f",
		    format_,
		    "-i",
		    input_,
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-fflags",
		    "nobuffer",
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		UpperCAmelCase_						=				_ffmpeg_stream(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		for item in iterator:
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							int:
		if stream_chunk_s is not None:
				UpperCAmelCase_						=				stream_chunk_s
		else:
				UpperCAmelCase_						=				chunk_length_s

		UpperCAmelCase_						=				ffmpeg_microphone(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     format_for_conversion=__SCREAMING_SNAKE_CASE )
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				np.intaa
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				np.floataa
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		if stride_length_s is None:
				UpperCAmelCase_						=				chunk_length_s / 6
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE			,     (int, float) ):
				UpperCAmelCase_						=				[stride_length_s, stride_length_s]

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Dict:
		UpperCAmelCase_						=				B""
		UpperCAmelCase_					,						UpperCAmelCase_						=				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}''' )
		UpperCAmelCase_						=				0
		for raw in iterator:
				acc += raw
				if stream and len(__SCREAMING_SNAKE_CASE ) < chunk_len:
						UpperCAmelCase_						=				(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(__SCREAMING_SNAKE_CASE ) >= chunk_len:
								# We are flushing the accumulator
								UpperCAmelCase_						=				(_stride_left, stride_right)
								UpperCAmelCase_						=				{"raw": acc[:chunk_len], "stride": stride}
								if stream:
										UpperCAmelCase_						=				False
								yield item
								UpperCAmelCase_						=				stride_left
								UpperCAmelCase_						=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(__SCREAMING_SNAKE_CASE ) > stride_left:
				UpperCAmelCase_						=				{"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						UpperCAmelCase_						=				False
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdout=subprocess.PIPE			,     bufsize=__SCREAMING_SNAKE_CASE ) as ffmpeg_process:
						while True:
								UpperCAmelCase_						=				ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE )
								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

