
"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

					A_				 =						[]
					for batch_idx in range(shape[0]				):
										values.append([]				)
										for _ in range(shape[1]				):
															values[-1].append(rng.random() * scale				)

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}






			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

								if equal_length:
													A_				 =						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
								else:
													# make sure that inputs increase in size
													A_				 =						[
													    floats_list((x, self.feature_size)	)
													    for x in range(self.min_seq_length       ,   self.max_seq_length       ,   self.seq_length_diff	)
													]
								if numpify:
													A_				 =						[np.asarray(_snake_case	) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						feat_extract_first.save_pretrained(_snake_case	)[0]
													check_json_file_has_correct_format(_snake_case	)
													A_				 =						self.feature_extraction_class.from_pretrained(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						os.path.join(_snake_case       ,   "feat_extract.json"	)
													feat_extract_first.to_json_file(_snake_case	)
													A_				 =						self.feature_extraction_class.from_json_file(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test batched
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test audio masking
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test 2-D numpy arrays are batched.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values
								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,   "clean"       ,   split="validation"	)
								# automatic decoding with librispeech
								A_				 =						ds.sort("id"	).select(range(_snake_case	)	)[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

								self.assertEquals(audio_values.shape       ,   (1, 1, 192, 128)	)

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)






"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:



		class __lowerCAmelCase				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					int       ,   **_snake_case						:					Optional[Any]	)      -> str:







										"""simple docstring"""
										pass
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						hashlib.mda(image.tobytes()				)
					return m.hexdigest()[:10]
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						np.array(__a				)
					A_				 =						npimg.shape
					return {"hash": hashimage(__a				), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
			snake_case	      =				dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Dict       ,   _snake_case						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						MaskGenerationPipeline(model=_snake_case       ,   image_processor=_snake_case	)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								pass




			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"	)
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						pipeline("mask-generation"       ,   model="facebook/sam-vit-huge"	)

								A_				 =						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
								    ]       ,   )
								# fmt: on






			@require_torch
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						"facebook/sam-vit-huge"
								A_				 =						pipeline("mask-generation"       ,   model=_snake_case	)

								A_				 =						image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"       ,   pred_iou_thresh=1       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								    ]       ,   )




