




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_snake_case										=       logging.get_logger(__name__)  # pylint: disable=invalid-name







class 						lowercase							(   lowerCAmelCase_		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							Optional[int]:
           super().__init__()

           if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""				)

           self.register_modules(
               speech_model=_snake_case		,     speech_processor=_snake_case		,     vae=_snake_case		,     text_encoder=_snake_case		,     tokenizer=_snake_case		,     unet=_snake_case		,     scheduler=_snake_case		,     feature_extractor=_snake_case		,     )

     def 					a__	(  self		,     _a = "auto"				)						->							Union[str, Any]:
           if slice_size == "auto":
                 _A							:     Any			=						self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(_snake_case				)

     def 					a__	(  self				)						->							Any:
           self.enable_attention_slicing(_snake_case				)



     @torch.no_grad()
     def __call__(  self		,     _a		,     _a=1_6000		,     _a = 512		,     _a = 512		,     _a = 50		,     _a = 7.5		,     _a = None		,     _a = 1		,     _a = 0.0		,     _a = None		,     _a = None		,     _a = "pil"		,     _a = True		,     _a = None		,     _a = 1		,     **_a		,     )						->							int:
           _A							:     Optional[int]			=						self.speech_processor.feature_extractor(
               _snake_case		,     return_tensors="""pt"""		,     sampling_rate=_snake_case				).input_features.to(self.device				)
           _A							:     Dict			=						self.speech_model.generate(_snake_case		,     max_length=48_0000				)

           _A							:     Optional[int]			=						self.speech_processor.tokenizer.batch_decode(_snake_case		,     skip_special_tokens=_snake_case		,     normalize=_snake_case				)[
               0
           ]

           if isinstance(_snake_case		,     _snake_case				):
                 _A							:     Union[str, Any]			=						1
           elif isinstance(_snake_case		,     _snake_case				):
                 _A							:     int			=						len(_snake_case				)
           else:
                 raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_snake_case				)}'''				)

           if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''				)

           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(_snake_case		,     _snake_case				) or callback_steps <= 0)
           ):
                 raise ValueError(
                     F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                     F''' {type(_snake_case				)}.'''				)

           # get prompt text embeddings
           _A							:     Dict			=						self.tokenizer(
               _snake_case		,     padding="""max_length"""		,     max_length=self.tokenizer.model_max_length		,     return_tensors="""pt"""		,     )
           _A							:     Dict			=						text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _A							:     List[Any]			=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
                 logger.warning(
                     """The following part of your input was truncated because CLIP can only handle sequences up to"""
                     F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''				)
                 _A							:     Union[str, Any]			=						text_input_ids[:, : self.tokenizer.model_max_length]
           _A							:     Optional[Any]			=						self.text_encoder(text_input_ids.to(self.device				)				)[0]

           # duplicate text embeddings for each generation per prompt, using mps friendly method
           _A							:     Optional[int]			=						text_embeddings.shape
           _A							:     Optional[int]			=						text_embeddings.repeat(1		,     _snake_case		,     1				)
           _A							:     str			=						text_embeddings.view(bs_embed * num_images_per_prompt		,     _snake_case		,     -1				)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           _A							:     Optional[Any]			=						guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                 _A							:     List[str]
                 if negative_prompt is None:
                       _A							:     Optional[Any]			=						[''] * batch_size
                 elif type(_snake_case				) is not type(_snake_case				):
                       raise TypeError(
                           F'''`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case				)} !='''
                           F''' {type(_snake_case				)}.'''				)
                 elif isinstance(_snake_case		,     _snake_case				):
                       _A							:     int			=						[negative_prompt]
                 elif batch_size != len(_snake_case				):
                       raise ValueError(
                           F'''`negative_prompt`: {negative_prompt} has batch size {len(_snake_case				)}, but `prompt`:'''
                           F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                           """ the batch size of `prompt`."""				)
                 else:
                       _A							:     Tuple			=						negative_prompt

                 _A							:     Dict			=						text_input_ids.shape[-1]
                 _A							:     int			=						self.tokenizer(
                     _snake_case		,     padding="""max_length"""		,     max_length=_snake_case		,     truncation=_snake_case		,     return_tensors="""pt"""		,     )
                 _A							:     Optional[int]			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _A							:     List[Any]			=						uncond_embeddings.shape[1]
                 _A							:     str			=						uncond_embeddings.repeat(1		,     _snake_case		,     1				)
                 _A							:     Union[str, Any]			=						uncond_embeddings.view(batch_size * num_images_per_prompt		,     _snake_case		,     -1				)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _A							:     Tuple			=						torch.cat([uncond_embeddings, text_embeddings]				)

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _A							:     str			=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           _A							:     str			=						text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       _A							:     Dict			=						torch.randn(_snake_case		,     generator=_snake_case		,     device="""cpu"""		,     dtype=_snake_case				).to(
                           self.device				)
                 else:
                       _A							:     str			=						torch.randn(_snake_case		,     generator=_snake_case		,     device=self.device		,     dtype=_snake_case				)
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''				)
                 _A							:     int			=						latents.to(self.device				)

           # set timesteps
           self.scheduler.set_timesteps(_snake_case				)

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           _A							:     Any			=						self.scheduler.timesteps.to(self.device				)

           # scale the initial noise by the standard deviation required by the scheduler
           _A							:     Any			=						latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _A							:     List[str]			=						'eta' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
           _A							:     Any			=						{}
           if accepts_eta:
                 _A							:     Optional[int]			=						eta

           for i, t in enumerate(self.progress_bar(_snake_case				)				):
                 # expand the latents if we are doing classifier free guidance
                 _A							:     List[Any]			=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                 _A							:     Tuple			=						self.scheduler.scale_model_input(_snake_case		,     _snake_case				)

                 # predict the noise residual
                 _A							:     str			=						self.unet(_snake_case		,     _snake_case		,     encoder_hidden_states=_snake_case				).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                       _A							:     List[str]			=						noise_pred.chunk(2				)
                       _A							:     Optional[int]			=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 _A							:     Union[str, Any]			=						self.scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                       callback(_snake_case		,     _snake_case		,     _snake_case				)

           _A							:     Optional[int]			=						1 / 0.18215 * latents
           _A							:     Tuple			=						self.vae.decode(_snake_case				).sample

           _A							:     Tuple			=						(image / 2 + 0.5).clamp(0		,     1				)

           # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
           _A							:     int			=						image.cpu().permute(0		,     2		,     3		,     1				).float().numpy()

           if output_type == "pil":
                 _A							:     str			=						self.numpy_to_pil(_snake_case				)

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=_snake_case		,     nsfw_content_detected=_snake_case				)






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


a_   :Any        =      random.Random()
def        lowercase_							(A : int		,					A : Union[str, Any]=1.0		,					A : List[str]=None		,					A : Any=None      ):
       if rng is None:
              snake_case__							:	List[str]   	=	global_rng

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

       return values




@require_torch
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Tuple=7, _snake_case  :  Union[str, Any]=4_0_0, _snake_case  :  Any=2_0_0_0, _snake_case  :  Dict=1, _snake_case  :  Optional[Any]=0.0, _snake_case  :  List[Any]=1_6_0_0_0, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=8_0, _snake_case  :  Dict=1_6, _snake_case  :  str=6_4, _snake_case  :  Tuple="hann_window", _snake_case  :  Union[str, Any]=8_0, _snake_case  :  Optional[Any]=7_6_0_0, _snake_case  :  str=1e-10, _snake_case  :  Any=True, )   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	parent
            snake_case__							:	Optional[Any]   	=	batch_size
            snake_case__							:	List[Any]   	=	min_seq_length
            snake_case__							:	List[Any]   	=	max_seq_length
            snake_case__							:	Any   	=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            snake_case__							:	Tuple   	=	feature_size
            snake_case__							:	List[Any]   	=	padding_value
            snake_case__							:	Any   	=	sampling_rate
            snake_case__							:	Dict   	=	do_normalize
            snake_case__							:	Union[str, Any]   	=	num_mel_bins
            snake_case__							:	Any   	=	hop_length
            snake_case__							:	Any   	=	win_length
            snake_case__							:	Any   	=	win_function
            snake_case__							:	Optional[int]   	=	fmin
            snake_case__							:	int   	=	fmax
            snake_case__							:	Union[str, Any]   	=	mel_floor
            snake_case__							:	Union[str, Any]   	=	return_attention_mask






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "do_normalize": self.do_normalize,
                "num_mel_bins": self.num_mel_bins,
                "hop_length": self.hop_length,
                "win_length": self.win_length,
                "win_function": self.win_function,
                "fmin": self.fmin,
                "fmax": self.fmax,
                "mel_floor": self.mel_floor,
                "return_attention_mask": self.return_attention_mask,
            }






     def    lowercase_   (       self  :  Any, _snake_case  :  Optional[Any]=False, _snake_case  :  List[str]=False					)   ->Union[str, Any]:
            def _flatten(_snake_case  :  List[str]					):
                   return list(itertools.chain(*_snake_case					)					)

            if equal_length:
                   snake_case__							:	Any   	=	floats_list((self.batch_size, self.max_seq_length)					)
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	int   	=	[
                       _flatten(floats_list((x, self.feature_size)					)					)
                       for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff					)
                   ]

            if numpify:
                   snake_case__							:	Any   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str=False, _snake_case  :  Dict=False					)   ->List[str]:
            if equal_length:
                   snake_case__							:	Optional[Any]   	=	[floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
            else:
                   # make sure that inputs increase in size
                   snake_case__							:	List[str]   	=	[
                       floats_list((x, self.num_mel_bins)					)
                       for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff					)
                   ]

            if numpify:
                   snake_case__							:	int   	=	[np.asarray(_snake_case					) for x in speech_inputs]

            return speech_inputs




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	SpeechTaFeatureExtractor






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	SpeechTaFeatureExtractionTester(self					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Dict					)   ->Any:
            self.assertTrue(np.all(np.mean(_snake_case, axis=0					) < 1e-3					)					)
            self.assertTrue(np.all(np.abs(np.var(_snake_case, axis=0					) - 1					) < 1e-3					)					)






     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	int   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Tuple   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test not batched input
            snake_case__							:	str   	=	feat_extract(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	List[str]   	=	feat_extract(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Any   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feat_extract(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  int					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]

            snake_case__							:	int   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	List[str]   	=	[None, 1_6_0_0, None]
            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	Optional[int]   	=	feat_extract(_snake_case, padding=_snake_case, max_length=_snake_case, return_tensors='np'					)
                   snake_case__							:	Optional[int]   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[Any]:
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Tuple   	=	range(8_0_0, 1_4_0_0, 2_0_0					)
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in lengths]

            snake_case__							:	Union[str, Any]   	=	['longest', 'max_length', 'do_not_pad']
            snake_case__							:	str   	=	[None, 1_6_0_0, None]

            for max_length, padding in zip(_snake_case, _snake_case					):
                   snake_case__							:	List[str]   	=	feat_extract(_snake_case, max_length=_snake_case, padding=_snake_case					)
                   snake_case__							:	Tuple   	=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
                   self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)






     def    lowercase_   (       self  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Any   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	str   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Optional[Any]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='max_length', return_tensors='np'					)
            snake_case__							:	int   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1]					)
            self._check_zero_mean_unit_variance(input_values[2]					)






     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	Dict   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	str   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=1_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Dict   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 1_0_0_0)					)

            snake_case__							:	Tuple   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	List[str]   	=	feat_extract(
                _snake_case, truncation=_snake_case, max_length=2_0_0_0, padding='longest', return_tensors='np'					)
            snake_case__							:	Optional[Any]   	=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2]					)

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 1_2_0_0)					)






     def    lowercase_   (       self  :  List[str]					)   ->Dict:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            snake_case__							:	List[Any]   	=	np.random.rand(1_0_0					).astype(np.floataa					)
            snake_case__							:	int   	=	np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                   snake_case__							:	int   	=	feature_extractor.pad([{'input_values': inputs}], return_tensors='np'					)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa					)
                   snake_case__							:	Optional[int]   	=	feature_extractor.pad([{'input_values': inputs}], return_tensors='pt'					)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case__							:	str   	=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
            # create three inputs of length 800, 1000, and 1200
            snake_case__							:	List[Any]   	=	[floats_list((1, x)					)[0] for x in range(8_0_0, 1_4_0_0, 2_0_0					)]
            snake_case__							:	Dict   	=	[np.asarray(_snake_case					) for speech_input in speech_inputs]

            # Test feature size
            snake_case__							:	Optional[int]   	=	feature_extractor(audio_target=_snake_case, padding=_snake_case, return_tensors='np'					).input_values
            self.assertTrue(input_values.ndim == 3					)
            self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

            # Test not batched input
            snake_case__							:	Dict   	=	feature_extractor(speech_inputs[0], return_tensors='np'					).input_values
            snake_case__							:	Any   	=	feature_extractor(np_speech_inputs[0], return_tensors='np'					).input_values
            self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test batched
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Dict   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)

            # Test 2-D numpy arrays are batched.
            snake_case__							:	Optional[Any]   	=	[floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
            snake_case__							:	int   	=	np.asarray(_snake_case					)
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            snake_case__							:	Union[str, Any]   	=	feature_extractor(_snake_case, return_tensors='np'					).input_values
            for enc_seq_a, enc_seq_a in zip(_snake_case, _snake_case					):
                   self.assertTrue(np.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[str]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Tuple   	=	BatchFeature({input_name: speech_inputs}					)

            self.assertTrue(all(len(_snake_case					) == len(_snake_case					) for x, y in zip(_snake_case, processed_features[input_name]					)					)					)

            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='np'					)

            snake_case__							:	Dict   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	List[str]   	=	batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	int   	=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_snake_case					)
            snake_case__							:	Optional[Any]   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Tuple   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[Any]   	=	BatchFeature({input_name: speech_inputs}, tensor_type='pt'					)

            snake_case__							:	Tuple   	=	processed_features[input_name]

            if len(batch_features_input.shape					) < 3:
                   snake_case__							:	Any   	=	batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)






     @require_torch
     def    lowercase_   (       self  :  Optional[int]					)   ->Tuple:
            snake_case__							:	Dict   	=	self.feature_extraction_class(**self.feat_extract_dict					)
            snake_case__							:	Union[str, Any]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	List[str]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	int   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)[input_name]
            snake_case__							:	Union[str, Any]   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='pt'					)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1e-2					)






     def    lowercase_   (       self  :  int					)   ->Any:
            snake_case__							:	Any   	=	self.feat_extract_dict
            snake_case__							:	List[Any]   	=	True
            snake_case__							:	Union[str, Any]   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	Any   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	List[Any]   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Union[str, Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)

            snake_case__							:	List[str]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	str   	=	feat_extract.pad(_snake_case, padding='longest', return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertListEqual(list(processed.attention_mask.shape					), list(processed[input_name].shape[:2]					)					)
            self.assertListEqual(processed.attention_mask.sum(-1					).tolist(), _snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->str:
            snake_case__							:	int   	=	self.feat_extract_dict
            snake_case__							:	List[str]   	=	True
            snake_case__							:	Tuple   	=	self.feature_extraction_class(**_snake_case					)
            snake_case__							:	List[str]   	=	self.feat_extract_tester.prepare_inputs_for_target()
            snake_case__							:	str   	=	[len(_snake_case					) for x in speech_inputs]
            snake_case__							:	Optional[Any]   	=	feat_extract.model_input_names[0]

            snake_case__							:	Optional[int]   	=	BatchFeature({input_name: speech_inputs}					)
            snake_case__							:	Optional[Any]   	=	min(_snake_case					)

            snake_case__							:	Union[str, Any]   	=	feat_extract.num_mel_bins  # hack!

            snake_case__							:	Tuple   	=	feat_extract.pad(
                _snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='np'					)
            self.assertIn('attention_mask', _snake_case					)
            self.assertListEqual(
                list(processed_pad.attention_mask.shape					), [processed_pad[input_name].shape[0], max_length]					)
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1					).tolist(), [max_length for x in speech_inputs]					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[int]					)   ->Optional[Any]:
            from datasets import load_dataset

            snake_case__							:	str   	=	load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation'					)
            # automatic decoding with librispeech
            snake_case__							:	Dict   	=	ds.sort('id'					).select(range(_snake_case					)					)[:num_samples]['audio']

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






     def    lowercase_   (       self  :  str					)   ->str:
            # fmt: off
            snake_case__							:	List[Any]   	=	torch.tensor(
                [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                 3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                 2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                 4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                 7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                 4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]					)
            # fmt: on

            snake_case__							:	Union[str, Any]   	=	self._load_datasamples(1					)
            snake_case__							:	Optional[int]   	=	SpeechTaFeatureExtractor()
            snake_case__							:	List[Any]   	=	feature_extractor(_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 9_3_6_8_0)					)
            self.assertTrue(torch.allclose(input_values[0, :3_0], _snake_case, atol=1e-6					)					)






     def    lowercase_   (       self  :  Any					)   ->str:
            # fmt: off
            snake_case__							:	Optional[Any]   	=	torch.tensor(
                [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
                 -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
                 -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
                 -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]					)
            # fmt: on

            snake_case__							:	List[str]   	=	self._load_datasamples(1					)
            snake_case__							:	str   	=	SpeechTaFeatureExtractor()
            snake_case__							:	Optional[Any]   	=	feature_extractor(audio_target=_snake_case, return_tensors='pt'					).input_values
            self.assertEquals(input_values.shape, (1, 3_6_6, 8_0)					)
            self.assertTrue(torch.allclose(input_values[0, 0, :3_0], _snake_case, atol=1e-4					)					)






