






"""simple docstring"""



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
						from transformers import WhisperFeatureExtractor

if is_torch_available():
						import torch

A__	:    List[Any]			 = random.Random()

def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Optional[int]=1.0    ,						lowerCamelCase__     :   int=None    ,						lowerCamelCase__     :   str=None  )    ->     Optional[Any]:
							if rng is None:
														lowerCamelCase_	:							List[str]									=global_rng

							lowerCamelCase_	:							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
@require_torchaudio
class        lowercase__      (							unittest.TestCase       ):


			def __init__(				self					:  int       ,	snake_case__					:  Any       ,	snake_case__					:  Optional[int]=7       ,	snake_case__					:  Optional[int]=400       ,	snake_case__					:  int=2000       ,	snake_case__					:  int=10       ,	snake_case__					:  Optional[int]=160       ,	snake_case__					:  Optional[Any]=8       ,	snake_case__					:  Optional[int]=0.0       ,	snake_case__					:  List[Any]=4000       ,	snake_case__					:  Union[str, Any]=False       ,	snake_case__					:  Union[str, Any]=True       ,	):
										lowerCamelCase_	:							str									=parent
										lowerCamelCase_	:							Optional[Any]									=batch_size
										lowerCamelCase_	:							int									=min_seq_length
										lowerCamelCase_	:							List[Any]									=max_seq_length
										lowerCamelCase_	:							List[str]									=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowerCamelCase_	:							Optional[int]									=padding_value
										lowerCamelCase_	:							Tuple									=sampling_rate
										lowerCamelCase_	:							List[Any]									=return_attention_mask
										lowerCamelCase_	:							List[Any]									=do_normalize
										lowerCamelCase_	:							Union[str, Any]									=feature_size
										lowerCamelCase_	:							Dict									=chunk_length
										lowerCamelCase_	:							List[str]									=hop_length


			def      UpperCAmelCase__						(				self					:  Tuple ):
										return {
										    "feature_size": self.feature_size,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  str=False       ,	snake_case__					:  Optional[int]=False ):
										def _flatten(snake_case__					:  Optional[int] ):
																	return list(itertools.chain(*snake_case__ ) )

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







@require_torch
@require_torchaudio
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Any             =			WhisperFeatureExtractor if is_speech_available() else None


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=WhisperFeatureExtractionTester(self )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Dict									=self.feature_extraction_class(**self.feat_extract_dict )

										with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCamelCase_	:							Any									=feat_extract_first.save_pretrained(snake_case__ )[0]
																	check_json_file_has_correct_format(snake_case__ )
																	lowerCamelCase_	:							Optional[int]									=self.feature_extraction_class.from_pretrained(snake_case__ )

										lowerCamelCase_	:							Dict									=feat_extract_first.to_dict()
										lowerCamelCase_	:							str									=feat_extract_second.to_dict()
										lowerCamelCase_	:							int									=feat_extract_first.mel_filters
										lowerCamelCase_	:							Optional[int]									=feat_extract_second.mel_filters
										self.assertTrue(np.allclose(snake_case__       ,	snake_case__ ) )
										self.assertEqual(snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							int									=self.feature_extraction_class(**self.feat_extract_dict )

										with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCamelCase_	:							List[Any]									=os.path.join(snake_case__       ,	"feat_extract.json" )
																	feat_extract_first.to_json_file(snake_case__ )
																	lowerCamelCase_	:							Dict									=self.feature_extraction_class.from_json_file(snake_case__ )

										lowerCamelCase_	:							Any									=feat_extract_first.to_dict()
										lowerCamelCase_	:							int									=feat_extract_second.to_dict()
										lowerCamelCase_	:							int									=feat_extract_first.mel_filters
										lowerCamelCase_	:							List[Any]									=feat_extract_second.mel_filters
										self.assertTrue(np.allclose(snake_case__       ,	snake_case__ ) )
										self.assertEqual(snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										# Tests that all call wrap to encode_plus and batch_encode_plus
										lowerCamelCase_	:							Union[str, Any]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										# create three inputs of length 800, 1000, and 1200
										lowerCamelCase_	:							List[str]									=[floats_list((1, x) )[0] for x in range(800       ,	1400       ,	200 )]
										lowerCamelCase_	:							Any									=[np.asarray(snake_case__ ) for speech_input in speech_inputs]

										# Test feature size
										lowerCamelCase_	:							Tuple									=feature_extractor(snake_case__       ,	padding="max_length"       ,	return_tensors="np" ).input_features
										self.assertTrue(input_features.ndim == 3 )
										self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
										self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )

										# Test not batched input
										lowerCamelCase_	:							Dict									=feature_extractor(speech_inputs[0]       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							List[str]									=feature_extractor(np_speech_inputs[0]       ,	return_tensors="np" ).input_features
										self.assertTrue(np.allclose(snake_case__       ,	snake_case__       ,	atol=1E-3 ) )

										# Test batched
										lowerCamelCase_	:							str									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							List[Any]									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										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.
										lowerCamelCase_	:							str									=[floats_list((1, x) )[0] for x in (800, 800, 800)]
										lowerCamelCase_	:							Any									=np.asarray(snake_case__ )
										lowerCamelCase_	:							str									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							int									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										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 truncation required
										lowerCamelCase_	:							Dict									=[floats_list((1, x) )[0] for x in range(200       ,	(feature_extractor.n_samples + 500)       ,	200 )]
										lowerCamelCase_	:							Dict									=[np.asarray(snake_case__ ) for speech_input in speech_inputs]

										lowerCamelCase_	:							List[str]									=[x[: feature_extractor.n_samples] for x in speech_inputs]
										lowerCamelCase_	:							Any									=[np.asarray(snake_case__ ) for speech_input in speech_inputs_truncated]

										lowerCamelCase_	:							Tuple									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										lowerCamelCase_	:							Union[str, Any]									=feature_extractor(snake_case__       ,	return_tensors="np" ).input_features
										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      UpperCAmelCase__						(				self					:  Any ):
										import torch

										lowerCamelCase_	:							List[str]									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Any									=np.random.rand(100       ,	32 ).astype(np.floataa )
										lowerCamelCase_	:							Optional[int]									=np_speech_inputs.tolist()

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


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

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


			def      UpperCAmelCase__						(				self					:  Tuple ):
										# fmt: off
										lowerCamelCase_	:							List[Any]									=torch.tensor(
										    [
										        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
										        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
										        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
										        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
										    ] )
										# fmt: on

										lowerCamelCase_	:							Dict									=self._load_datasamples(1 )
										lowerCamelCase_	:							Union[str, Any]									=WhisperFeatureExtractor()
										lowerCamelCase_	:							Union[str, Any]									=feature_extractor(snake_case__       ,	return_tensors="pt" ).input_features
										self.assertEqual(input_features.shape       ,	(1, 80, 3000) )
										self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,	snake_case__       ,	atol=1E-4 ) )


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Any									=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
										lowerCamelCase_	:							Tuple									=self._load_datasamples(1 )[0]
										lowerCamelCase_	:							Optional[Any]									=((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
										lowerCamelCase_	:							Optional[Any]									=feat_extract.zero_mean_unit_var_norm([audio]       ,	attention_mask=snake_case__ )[0]

										self.assertTrue(np.all(np.mean(snake_case__ ) < 1E-3 ) )
										self.assertTrue(np.all(np.abs(np.var(snake_case__ ) - 1 ) < 1E-3 ) )











"""simple docstring"""



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 			_snake_case    (					lowerCamelCase__     :   int=None  )    ->     Union[str, Any]:
							if subparsers is not None:
														lowerCamelCase_	:							List[Any]									=subparsers.add_parser("test"  )
							else:
														lowerCamelCase_	:							List[str]									=argparse.ArgumentParser("Accelerate test command"  )

							parser.add_argument(
							    "--config_file"    ,						default=lowerCamelCase__    ,						help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )    ,						)

							if subparsers is not None:
														parser.set_defaults(func=lowerCamelCase__  )
							return parser

def 			_snake_case    (					lowerCamelCase__     :   List[Any]  )    ->     Any:
							lowerCamelCase_	:							Optional[Any]									=os.path.sep.join(__file__.split(os.path.sep  )[:-2] + ["test_utils", "scripts", "test_script.py"]  )

							if args.config_file is None:
														lowerCamelCase_	:							List[Any]									=script_name
							else:
														lowerCamelCase_	:							Union[str, Any]									=F"""--config_file={args.config_file} {script_name}"""

							lowerCamelCase_	:							List[str]									=["accelerate-launch"] + test_args.split()
							lowerCamelCase_	:							Tuple									=execute_subprocess_async(lowerCamelCase__    ,						env=os.environ.copy()  )
							if result.returncode == 0:
														print("Test is a success! You are ready for your distributed training!"  )

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Any									=test_command_parser()
							lowerCamelCase_	:							List[Any]									=parser.parse_args()
							test_command(lowerCamelCase__  )


if __name__ == "__main__":
						main()



