





'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase_					= random.Random()

if is_torch_available():
      import torch





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Dict=1.0					,       __lowerCamelCase :    int=None					,       __lowerCamelCase :    List[str]=None    )						->Tuple:
    if rng is None:
        _SCREAMING_SNAKE_CASE									=			global_rng

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

    return values


class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=7 ,		A=400 ,		A=2000 ,		A=1 ,		A=0.0 ,		A=1_6000 ,		A=True ,		A=True ,		)		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			min_seq_length
        _SCREAMING_SNAKE_CASE									=			max_seq_length
        _SCREAMING_SNAKE_CASE									=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _SCREAMING_SNAKE_CASE									=			feature_size
        _SCREAMING_SNAKE_CASE									=			padding_value
        _SCREAMING_SNAKE_CASE									=			sampling_rate
        _SCREAMING_SNAKE_CASE									=			return_attention_mask
        _SCREAMING_SNAKE_CASE									=			do_normalize


    def 			snake_case_(		self       )		->   Dict:
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }





    def 			snake_case_(		self ,		A=False ,		A=False       )		->   str:
        def _flatten(A       ):
            return list(itertools.chain(*A       )       )

        if equal_length:
            _SCREAMING_SNAKE_CASE									=			floats_list((self.batch_size, self.max_seq_length)       )
        else:
            # make sure that inputs increase in size
            _SCREAMING_SNAKE_CASE									=			[
                _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:
            _SCREAMING_SNAKE_CASE									=			[np.asarray(A       ) for x in speech_inputs]

        return speech_inputs




@require_torch
@require_torchaudio
class 					a_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = ASTFeatureExtractor


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			ASTFeatureExtractionTester(self       )


    def 			snake_case_(		self       )		->   Dict:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _SCREAMING_SNAKE_CASE									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        # create three inputs of length 800, 1000, and 1200
        _SCREAMING_SNAKE_CASE									=			[floats_list((1, x)       )[0] for x in range(800 ,		1400 ,		200       )]
        _SCREAMING_SNAKE_CASE									=			[np.asarray(A       ) for speech_input in speech_inputs]

        # Test not batched input
        _SCREAMING_SNAKE_CASE									=			feat_extract(speech_inputs[0] ,		return_tensors="""np"""       ).input_values
        _SCREAMING_SNAKE_CASE									=			feat_extract(np_speech_inputs[0] ,		return_tensors="""np"""       ).input_values
        self.assertTrue(np.allclose(A ,		A ,		atol=1e-3       )       )

        # Test batched
        _SCREAMING_SNAKE_CASE									=			feat_extract(A ,		padding=A ,		return_tensors="""np"""       ).input_values
        _SCREAMING_SNAKE_CASE									=			feat_extract(A ,		padding=A ,		return_tensors="""np"""       ).input_values
        for enc_seq_a, enc_seq_a in zip(A ,		A       ):
            self.assertTrue(np.allclose(A ,		A ,		atol=1e-3       )       )

        # Test 2-D numpy arrays are batched.
        _SCREAMING_SNAKE_CASE									=			[floats_list((1, x)       )[0] for x in (800, 800, 800)]
        _SCREAMING_SNAKE_CASE									=			np.asarray(A       )
        _SCREAMING_SNAKE_CASE									=			feat_extract(A ,		return_tensors="""np"""       ).input_values
        _SCREAMING_SNAKE_CASE									=			feat_extract(A ,		return_tensors="""np"""       ).input_values
        for enc_seq_a, enc_seq_a in zip(A ,		A       ):
            self.assertTrue(np.allclose(A ,		A ,		atol=1e-3       )       )


    @require_torch
    def 			snake_case_(		self       )		->   List[Any]:
        import torch

        _SCREAMING_SNAKE_CASE									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        _SCREAMING_SNAKE_CASE									=			np.random.rand(100       ).astype(np.floataa       )
        _SCREAMING_SNAKE_CASE									=			np_speech_inputs.tolist()

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


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        from datasets import load_dataset

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

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





    @require_torch
    def 			snake_case_(		self       )		->   Optional[int]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			torch.tensor(
            [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
             -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
             -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
             -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]       )
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			self._load_datasamples(1       )
        _SCREAMING_SNAKE_CASE									=			ASTFeatureExtractor()
        _SCREAMING_SNAKE_CASE									=			feature_extractor(A ,		return_tensors="""pt"""       ).input_values
        self.assertEquals(input_values.shape ,		(1, 1024, 128)       )
        self.assertTrue(torch.allclose(input_values[0, 0, :30] ,		A ,		atol=1e-4       )       )










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



