





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 				a_       (    unittest.TestCase							):





     def __init__(			self		: List[str]     ,				lowercase		: List[Any]     ,				lowercase		: str=7     ,				lowercase		: Tuple=3     ,				lowercase		: str=30     ,				lowercase		: Optional[int]=400     ,				lowercase		: Optional[Any]=True     ,				lowercase		: Optional[int]=None     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: Tuple=[0.5, 0.5, 0.5]     ,				lowercase		: Union[str, Any]=[0.5, 0.5, 0.5]     ,				lowercase		: List[str]=True     ,				lowercase		: Optional[int]=1 / 255     ,				lowercase		: Optional[int]=True     ,				):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        lowercase_      :Dict         =			parent
        lowercase_      :Tuple         =			batch_size
        lowercase_      :Optional[Any]         =			num_channels
        lowercase_      :Any         =			min_resolution
        lowercase_      :Union[str, Any]         =			max_resolution
        lowercase_      :str         =			do_resize
        lowercase_      :Dict         =			size
        lowercase_      :Optional[Any]         =			do_normalize
        lowercase_      :Tuple         =			image_mean
        lowercase_      :Union[str, Any]         =			image_std
        lowercase_      :Any         =			do_rescale
        lowercase_      :int         =			rescale_factor
        lowercase_      :int         =			do_pad





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        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 							lowercase__				(			self		: Dict     ,				lowercase		: Tuple     ,				lowercase		: Dict=False						):





        """simple docstring"""




        if not batched:
           lowercase_      :Union[str, Any]         =			image_inputs[0]
           if isinstance(UpperCamelCase__     ,				Image.Image						):
              lowercase_      ,							lowercase_      :List[str]         =			image.size
           else:
              lowercase_      ,							lowercase_      :int         =			image.shape[1], image.shape[2]
           if w < h:
              lowercase_      :Any         =			int(self.size["shortest_edge"] * h / w						)
              lowercase_      :Any         =			self.size["shortest_edge"]
           elif w > h:
              lowercase_      :List[str]         =			self.size["shortest_edge"]
              lowercase_      :Union[str, Any]         =			int(self.size["shortest_edge"] * w / h						)
           else:
              lowercase_      :int         =			self.size["shortest_edge"]
              lowercase_      :Optional[Any]         =			self.size["shortest_edge"]

        else:
           lowercase_      :Optional[Any]         =			[]
           for image in image_inputs:
              lowercase_      ,							lowercase_      :List[Any]         =			self.get_expected_values([image]						)
              expected_values.append((expected_height, expected_width)						)
           lowercase_      :Union[str, Any]         =			max(UpperCamelCase__     ,				key=lambda lowercase						: item[0]						)[0]
           lowercase_      :Dict         =			max(UpperCamelCase__     ,				key=lambda lowercase						: item[1]						)[1]

        return expected_height, expected_width







@require_torch
@require_vision
class 				a_       (    __a     ,						unittest.TestCase							):
     __A								=      ConditionalDetrImageProcessor if is_vision_available() else None





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			ConditionalDetrImageProcessingTester(self						)





     @property
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        return self.image_processor_tester.prepare_image_processor_dict()





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :str         =			self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(UpperCamelCase__     ,				"image_mean"						)						)
        self.assertTrue(hasattr(UpperCamelCase__     ,				"image_std"						)						)
        self.assertTrue(hasattr(UpperCamelCase__     ,				"do_normalize"						)						)
        self.assertTrue(hasattr(UpperCamelCase__     ,				"do_resize"						)						)
        self.assertTrue(hasattr(UpperCamelCase__     ,				"size"						)						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict						)
        self.assertEqual(image_processor.size     ,				{"shortest_edge": 18, "longest_edge": 1_333}						)
        self.assertEqual(image_processor.do_pad     ,				UpperCamelCase__						)

        lowercase_      :Optional[int]         =			self.image_processing_class.from_dict(
            self.image_processor_dict     ,				size=42     ,				max_size=84     ,				pad_and_return_pixel_mask=UpperCamelCase__						)
        self.assertEqual(image_processor.size     ,				{"shortest_edge": 42, "longest_edge": 84}						)
        self.assertEqual(image_processor.do_pad     ,				UpperCamelCase__						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        pass





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        lowercase_      :str         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=UpperCamelCase__						)
        for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__     ,				Image.Image						)

        # Test not batched input
        lowercase_      :Optional[Any]         =			image_processing(image_inputs[0]     ,				return_tensors="pt"						).pixel_values

        lowercase_      ,							lowercase_      :Dict         =			self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

        # Test batched
        lowercase_      ,							lowercase_      :Optional[Any]         =			self.image_processor_tester.get_expected_values(UpperCamelCase__     ,				batched=UpperCamelCase__						)

        lowercase_      :Optional[Any]         =			image_processing(UpperCamelCase__     ,				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 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Dict         =			self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        lowercase_      :Dict         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=UpperCamelCase__     ,				numpify=UpperCamelCase__						)
        for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__     ,				np.ndarray						)

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

        lowercase_      ,							lowercase_      :Optional[Any]         =			self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

        # Test batched
        lowercase_      :Optional[int]         =			image_processing(UpperCamelCase__     ,				return_tensors="pt"						).pixel_values

        lowercase_      ,							lowercase_      :Optional[Any]         =			self.image_processor_tester.get_expected_values(UpperCamelCase__     ,				batched=UpperCamelCase__						)

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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        lowercase_      :str         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=UpperCamelCase__     ,				torchify=UpperCamelCase__						)
        for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__     ,				torch.Tensor						)

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

        lowercase_      ,							lowercase_      :Union[str, Any]         =			self.image_processor_tester.get_expected_values(UpperCamelCase__						)

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

        # Test batched
        lowercase_      :int         =			image_processing(UpperCamelCase__     ,				return_tensors="pt"						).pixel_values

        lowercase_      ,							lowercase_      :Optional[Any]         =			self.image_processor_tester.get_expected_values(UpperCamelCase__     ,				batched=UpperCamelCase__						)

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





     @slow
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Dict         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,				"r"						) as f:
           lowercase_      :Union[str, Any]         =			json.loads(f.read()						)

        lowercase_      :Optional[int]         =			{"image_id": 39_769, "annotations": target}

        # encode them
        lowercase_      :str         =			ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
        lowercase_      :Any         =			image_processing(images=UpperCamelCase__     ,				annotations=UpperCamelCase__     ,				return_tensors="pt"						)

        # verify pixel values
        lowercase_      :int         =			torch.Size([1, 3, 800, 1_066]						)
        self.assertEqual(encoding["pixel_values"].shape     ,				UpperCamelCase__						)

        lowercase_      :List[Any]         =			torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,				UpperCamelCase__     ,				atol=1e-4						)						)

        # verify area
        lowercase_      :Optional[Any]         =			torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,				UpperCamelCase__						)						)
        # verify boxes
        lowercase_      :Union[str, Any]         =			torch.Size([6, 4]						)
        self.assertEqual(encoding["labels"][0]["boxes"].shape     ,				UpperCamelCase__						)
        lowercase_      :Dict         =			torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,				UpperCamelCase__     ,				atol=1e-3						)						)
        # verify image_id
        lowercase_      :List[Any]         =			torch.tensor([39_769]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,				UpperCamelCase__						)						)
        # verify is_crowd
        lowercase_      :Dict         =			torch.tensor([0, 0, 0, 0, 0, 0]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,				UpperCamelCase__						)						)
        # verify class_labels
        lowercase_      :Optional[Any]         =			torch.tensor([75, 75, 63, 65, 17, 17]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,				UpperCamelCase__						)						)
        # verify orig_size
        lowercase_      :Optional[int]         =			torch.tensor([480, 640]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,				UpperCamelCase__						)						)
        # verify size
        lowercase_      :List[Any]         =			torch.tensor([800, 1_066]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,				UpperCamelCase__						)						)







     @slow
     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,				"r"						) as f:
           lowercase_      :Dict         =			json.loads(f.read()						)

        lowercase_      :List[Any]         =			{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

        lowercase_      :Union[str, Any]         =			pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

        # encode them
        lowercase_      :Tuple         =			ConditionalDetrImageProcessor(format="coco_panoptic"						)
        lowercase_      :List[str]         =			image_processing(images=UpperCamelCase__     ,				annotations=UpperCamelCase__     ,				masks_path=UpperCamelCase__     ,				return_tensors="pt"						)

        # verify pixel values
        lowercase_      :List[str]         =			torch.Size([1, 3, 800, 1_066]						)
        self.assertEqual(encoding["pixel_values"].shape     ,				UpperCamelCase__						)

        lowercase_      :List[str]         =			torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,				UpperCamelCase__     ,				atol=1e-4						)						)

        # verify area
        lowercase_      :Any         =			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,				UpperCamelCase__						)						)
        # verify boxes
        lowercase_      :List[Any]         =			torch.Size([6, 4]						)
        self.assertEqual(encoding["labels"][0]["boxes"].shape     ,				UpperCamelCase__						)
        lowercase_      :Any         =			torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,				UpperCamelCase__     ,				atol=1e-3						)						)
        # verify image_id
        lowercase_      :int         =			torch.tensor([39_769]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,				UpperCamelCase__						)						)
        # verify is_crowd
        lowercase_      :Optional[int]         =			torch.tensor([0, 0, 0, 0, 0, 0]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,				UpperCamelCase__						)						)
        # verify class_labels
        lowercase_      :Union[str, Any]         =			torch.tensor([17, 17, 63, 75, 75, 93]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,				UpperCamelCase__						)						)
        # verify masks
        lowercase_      :List[str]         =			822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,				UpperCamelCase__						)
        # verify orig_size
        lowercase_      :List[Any]         =			torch.tensor([480, 640]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,				UpperCamelCase__						)						)
        # verify size
        lowercase_      :Dict         =			torch.tensor([800, 1_066]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,				UpperCamelCase__						)						)









'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			spectrogram(
            lowercase     ,				window_function(self.n_fft     ,				"hann"						)     ,				frame_length=self.n_fft     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=self.mel_filters     ,				log_mel="log10"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(log_spec + 4.0) / 4.0
        return log_spec





     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							lowercase__				(			lowercase		: List[np.ndarray]     ,				lowercase		: List[np.ndarray]     ,				lowercase		: float = 0.0						):





        """simple docstring"""




        if attention_mask is not None:
           lowercase_      :Optional[int]         =			np.array(lowercase     ,				np.intaa						)
           lowercase_      :Any         =			[]

           for vector, length in zip(lowercase     ,				attention_mask.sum(-1						)						):
              lowercase_      :Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
              if length < normed_slice.shape[0]:
                 lowercase_      :List[Any]         =			padding_value

              normed_input_values.append(lowercase						)
        else:
           lowercase_      :List[Any]         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

        return normed_input_values





     def __call__(			self		: Tuple     ,				lowercase		: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,				lowercase		: bool = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "max_length"     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
              raise ValueError(
                  F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                  F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                  F' was sampled with {self.sampling_rate} and not {sampling_rate}.'						)
        else:
           logger.warning(
               "It is strongly recommended to pass the `sampling_rate` argument to this function. "
               "Failing to do so can result in silent errors that might be hard to debug."						)

        lowercase_      :List[str]         =			isinstance(lowercase     ,				np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
           raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

        if is_batched:
           lowercase_      :Any         =			[np.asarray([speech]     ,				dtype=np.floataa						).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase     ,				np.ndarray						):
           lowercase_      :List[Any]         =			np.asarray(lowercase     ,				dtype=np.floataa						)
        elif isinstance(lowercase     ,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           lowercase_      :Union[str, Any]         =			raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
           lowercase_      :Optional[int]         =			[np.asarray([raw_speech]						).T]

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			self.pad(
            lowercase     ,				padding=lowercase     ,				max_length=max_length if max_length else self.n_samples     ,				truncation=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=return_attention_mask or do_normalize     ,				)

        # zero-mean and unit-variance normalization
        if do_normalize:
           lowercase_      :Union[str, Any]         =			self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]     ,				attention_mask=padded_inputs["attention_mask"]     ,				padding_value=self.padding_value     ,				)
           lowercase_      :List[Any]         =			np.stack(padded_inputs["input_features"]     ,				axis=0						)

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

        if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowercase_      :Dict         =			padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


