

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

a_      =     random.Random()

def 			lowerCamelCase__			(       _a				,      _a=1.0				,      _a=None				,      _a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE :   List[str]					  =							global_rng

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

	return values






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			str   ,   a				:			Any   ,   a				:			Union[str, Any]=7   ,   a				:			List[Any]=400   ,   a				:			str=2000   ,   a				:			Dict=2048   ,   a				:			List[Any]=128   ,   a				:			Tuple=1   ,   a				:			Union[str, Any]=512   ,   a				:			List[str]=30   ,   a				:			Tuple=4_4100   ,   )				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							parent
				SCREAMING_SNAKE_CASE :   List[str]					  =							batch_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							min_seq_length
				SCREAMING_SNAKE_CASE :   List[str]					  =							max_seq_length
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				SCREAMING_SNAKE_CASE :   Dict					  =							spectrogram_length
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_audio_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hop_length
				SCREAMING_SNAKE_CASE :   List[Any]					  =							chunk_length
				SCREAMING_SNAKE_CASE :   List[str]					  =							sampling_rate
			def 				__UpperCamelCase			(      self				:			Optional[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 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			int=False   ,   a				:			Tuple=False						)				->							Union[str, Any]:

				"""simple docstring"""

				def _flatten(a				:			Any						):
					return list(itertools.chain(*a						)						)

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






@require_torch
@require_torchaudio
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =TvltFeatureExtractor
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							TvltFeatureExtractionTester(self						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)
				self.assertTrue(hasattr(a   ,   "spectrogram_length"						)						)
				self.assertTrue(hasattr(a   ,   "feature_size"						)						)
				self.assertTrue(hasattr(a   ,   "num_audio_channels"						)						)
				self.assertTrue(hasattr(a   ,   "hop_length"						)						)
				self.assertTrue(hasattr(a   ,   "chunk_length"						)						)
				self.assertTrue(hasattr(a   ,   "sampling_rate"						)						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							feat_extract_first.save_pretrained(a						)[0]
					check_json_file_has_correct_format(a						)
					SCREAMING_SNAKE_CASE :   Any					  =							self.feature_extraction_class.from_pretrained(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   int					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   str					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				with tempfile.TemporaryDirectory() as tmpdirname:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							os.path.join(a   ,   "feat_extract.json"						)
					feat_extract_first.to_json_file(a						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.feature_extraction_class.from_json_file(a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							feat_extract_first.to_dict()
				SCREAMING_SNAKE_CASE :   Tuple					  =							feat_extract_second.to_dict()
				SCREAMING_SNAKE_CASE :   str					  =							dict_first.pop("mel_filters"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							dict_second.pop("mel_filters"						)
				self.assertTrue(np.allclose(a   ,   a						)						)
				self.assertEqual(a   ,   a						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.feature_extraction_class(**self.feat_extract_dict						)

				# create three inputs of length 800, 1000, and 1200
				SCREAMING_SNAKE_CASE :   str					  =							[floats_list((1, x)						)[0] for x in range(800   ,   1400   ,   200						)]
				SCREAMING_SNAKE_CASE :   str					  =							[np.asarray(a						) for speech_input in speech_inputs]

				# Test not batched input
				SCREAMING_SNAKE_CASE :   str					  =							feature_extractor(np_speech_inputs[0]   ,   return_tensors="np"   ,   sampling_rate=4_4100						).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
				SCREAMING_SNAKE_CASE :   Tuple					  =							feature_extractor(a   ,   return_tensors="np"   ,   sampling_rate=4_4100						).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
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							feature_extractor(
				    a   ,   return_tensors="np"   ,   sampling_rate=4_4100   ,   mask_audio=a						).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.
				SCREAMING_SNAKE_CASE :   Any					  =							[floats_list((1, x)						)[0] for x in (800, 800, 800)]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np.asarray(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							feature_extractor(a   ,   return_tensors="np"   ,   sampling_rate=4_4100						).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 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,   "clean"   ,   split="validation"						)
				# automatic decoding with librispeech
				SCREAMING_SNAKE_CASE :   Dict					  =							ds.sort("id"						).select(range(a						)						)[:num_samples]["audio"]

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






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self._load_datasamples(1						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							TvltFeatureExtractor()
				SCREAMING_SNAKE_CASE :   Any					  =							feature_extractor(a   ,   return_tensors="pt"						).audio_values

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

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]						)
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,   a   ,   atol=1e-4						)						)


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				['''pixel_values''']




	def __init__(     self						:     Dict					,      __A						:     bool = True					,      __A						:     Optional[Dict[str, int]] = None					,      __A						:     PILImageResampling = PILImageResampling.BILINEAR					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      **__A						:     int					,      ):
		super().__init__(**__A							)
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 256}
		__A :			Dict		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			str		    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			str		    =			do_resize
		__A :			Dict		    =			size
		__A :			Any		    =			resample
		__A :			Optional[Any]		    =			do_center_crop
		__A :			List[str]		    =			crop_size
		__A :			Optional[int]		    =			do_rescale
		__A :			int		    =			rescale_factor
		__A :			Union[str, Any]		    =			do_normalize
		__A :			int		    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		__A :			Dict		    =			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     PILImageResampling = PILImageResampling.BICUBIC					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     List[Any]					,      ):
		__A :			str		    =			get_size_dict(__A					,      default_to_square=__A							)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		__A :			Dict		    =			get_resize_output_image_size(__A					,      size=size["""shortest_edge"""]					,      default_to_square=__A							)
		return resize(__A					,      size=__A					,      resample=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     str					,      ):
		__A :			str		    =			get_size_dict(__A							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""							)
		return center_crop(__A					,      size=(size["""height"""], size["""width"""])					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     np.ndarray					,      __A						:     float					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Optional[int]							):
		return rescale(__A					,      scale=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     np.ndarray					,      __A						:     Union[float, List[float]]					,      __A						:     Union[float, List[float]]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Tuple					,      ):
		return normalize(__A					,      mean=__A					,      std=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     ImageInput					,      __A						:     Optional[bool] = None					,      __A						:     Dict[str, int] = None					,      __A						:     PILImageResampling = None					,      __A						:     bool = None					,      __A						:     Dict[str, int] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[float] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[str, TensorType]] = None					,      __A						:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__A						:     Optional[int]					,      ):
		__A :			List[str]		    =			do_resize if do_resize is not None else self.do_resize
		__A :			Any		    =			size if size is not None else self.size
		__A :			Union[str, Any]		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			Tuple		    =			resample if resample is not None else self.resample
		__A :			Tuple		    =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A :			List[Any]		    =			crop_size if crop_size is not None else self.crop_size
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			Tuple		    =			do_rescale if do_rescale is not None else self.do_rescale
		__A :			Optional[Any]		    =			rescale_factor if rescale_factor is not None else self.rescale_factor
		__A :			Optional[int]		    =			do_normalize if do_normalize is not None else self.do_normalize
		__A :			Optional[int]		    =			image_mean if image_mean is not None else self.image_mean
		__A :			List[str]		    =			image_std if image_std is not None else self.image_std

		__A :			Union[str, Any]		    =			make_list_of_images(__A							)

		if not valid_images(__A							):
			raise ValueError(
			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
			    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

		if do_resize and size is None:
			raise ValueError("""Size must be specified if do_resize is True."""							)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

		if do_rescale and rescale_factor is None:
			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

		# All transformations expect numpy arrays.
		__A :			Union[str, Any]		    =			[to_numpy_array(__A							) for image in images]

		if do_resize:
			__A :			int		    =			[self.resize(image=__A					,      size=__A					,      resample=__A							) for image in images]

		if do_center_crop:
			__A :			Optional[Any]		    =			[self.center_crop(image=__A					,      size=__A							) for image in images]

		if do_rescale:
			__A :			List[Any]		    =			[self.rescale(image=__A					,      scale=__A							) for image in images]

		if do_normalize:
			__A :			Any		    =			[self.normalize(image=__A					,      mean=__A					,      std=__A							) for image in images]

		__A :			int		    =			[to_channel_dimension_format(__A					,      __A							) for image in images]

		__A :			Tuple		    =			{"""pixel_values""": images}
		return BatchFeature(data=__A					,      tensor_type=__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[Tuple] = None							):
		__A :			Union[str, Any]		    =			outputs.logits

		# Resize logits and compute semantic segmentation maps
		if target_sizes is not None:
			if len(__A							) != len(__A							):
				raise ValueError(
				    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

			if is_torch_tensor(__A							):
				__A :			str		    =			target_sizes.numpy()

			__A :			int		    =			[]

			for idx in range(len(__A							)							):
				__A :			Any		    =			torch.nn.functional.interpolate(
				    logits[idx].unsqueeze(dim=0							)					,      size=target_sizes[idx]					,      mode="""bilinear"""					,      align_corners=__A							)
				__A :			Union[str, Any]		    =			resized_logits[0].argmax(dim=0							)
				semantic_segmentation.append(__A							)
		else:
			__A :			List[str]		    =			logits.argmax(dim=1							)
			__A :			Tuple		    =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

		return semantic_segmentation





