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

SCREAMING_SNAKE_CASE__		:					Dict						=					random.Random()







def     _a				(      lowercase__  :			Any     ,  lowercase__  :			Tuple=1.0     ,  lowercase__  :			int=None     ,  lowercase__  :			int=None ):







     '''simple docstring'''


     if rng is None:
          SCREAMING_SNAKE_CASE__				:				int			=		global_rng

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

     return values

class       snake_case					(      unittest.TestCase						):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		min_seq_length
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		max_seq_length
       SCREAMING_SNAKE_CASE__				:				List[str]			=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       SCREAMING_SNAKE_CASE__				:				Any			=		spectrogram_length
       SCREAMING_SNAKE_CASE__				:				Dict			=		feature_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		num_audio_channels
       SCREAMING_SNAKE_CASE__				:				Any			=		hop_length
       SCREAMING_SNAKE_CASE__				:				int			=		chunk_length
       SCREAMING_SNAKE_CASE__				:				Dict			=		sampling_rate




  def 	__lowercase(      self     :	int				)->       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 	__lowercase(      self     :	int     , a_     :	Any=False     , a_     :	Union[str, Any]=False				)->       str:




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

       if equal_length:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		[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__				:				int			=		[np.asarray(a_				) for x in speech_inputs]
       return speech_inputs




@require_torch
@require_torchaudio
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     TvltFeatureExtractor




  def 	__lowercase(      self     :	List[Any]				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		TvltFeatureExtractionTester(self				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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 	__lowercase(      self     :	int				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.feature_extraction_class(**self.feat_extract_dict				)

       with tempfile.TemporaryDirectory() as tmpdirname:
            SCREAMING_SNAKE_CASE__				:				Any			=		feat_extract_first.save_pretrained(a_				)[0]
            check_json_file_has_correct_format(a_				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.feature_extraction_class.from_pretrained(a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_extract_first.to_dict()
       SCREAMING_SNAKE_CASE__				:				str			=		feat_extract_second.to_dict()
       SCREAMING_SNAKE_CASE__				:				Dict			=		dict_first.pop('mel_filters'				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		dict_second.pop('mel_filters'				)
       self.assertTrue(np.allclose(a_     , a_				)				)
       self.assertEqual(a_     , a_				)




  def 	__lowercase(      self     :	List[str]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.feature_extraction_class(**self.feat_extract_dict				)

       with tempfile.TemporaryDirectory() as tmpdirname:
            SCREAMING_SNAKE_CASE__				:				str			=		os.path.join(a_     , 'feat_extract.json'				)
            feat_extract_first.to_json_file(a_				)
            SCREAMING_SNAKE_CASE__				:				int			=		self.feature_extraction_class.from_json_file(a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		feat_extract_first.to_dict()
       SCREAMING_SNAKE_CASE__				:				int			=		feat_extract_second.to_dict()
       SCREAMING_SNAKE_CASE__				:				Any			=		dict_first.pop('mel_filters'				)
       SCREAMING_SNAKE_CASE__				:				Any			=		dict_second.pop('mel_filters'				)
       self.assertTrue(np.allclose(a_     , a_				)				)
       self.assertEqual(a_     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.feature_extraction_class(**self.feat_extract_dict				)

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

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Tuple			=		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__				:				List[Any]			=		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__				:				List[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__				:				List[str]			=		np.asarray(a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		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 	__lowercase(      self     :	Any     , a_     :	List[str]				)->       Optional[Any]:




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

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._load_datasamples(1				)
       SCREAMING_SNAKE_CASE__				:				Any			=		TvltFeatureExtractor()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		feature_extractor(a_     , return_tensors='pt'				).audio_values

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]				)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     , a_     , atol=1e-4				)				)




# Copyright 2023 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 torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



