
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_A													=				random.Random()



def 		lowercase_    (     A__				,	A__=1.0				,	A__=None				,	A__=None	)							->							Optional[int]:
    """simple docstring"""

    if rng is None:
        snake_case				  =      global_rng

    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   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[int]       ,					_A   :					List[str]=7       ,					_A   :					Optional[int]=4_0_0       ,					_A   :					Optional[int]=2_0_0_0       ,					_A   :					List[str]=1       ,					_A   :					int=0.0       ,					_A   :					str=1_6_0_0_0       ,					_A   :					List[Any]=True       ,					_A   :					Optional[int]=True       ,					)		->		List[Any]:
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      min_seq_length
         snake_case				  =      max_seq_length
         snake_case				  =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         snake_case				  =      feature_size
         snake_case				  =      padding_value
         snake_case				  =      sampling_rate
         snake_case				  =      return_attention_mask
         snake_case				  =      do_normalize





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
         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 	UpperCAmelCase(self   :					Tuple       ,					_A   :					int=False       ,					_A   :					Optional[Any]=False     )		->		Dict:
         def _flatten(_A   :					int     ):
             return list(itertools.chain(*_A     )     )

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

         return speech_inputs







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	WavaVecaFeatureExtractor





     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         snake_case				  =      WavaVecaFeatureExtractionTester(self     )





     def 	UpperCAmelCase(self   :					str       ,					_A   :					Optional[Any]     )		->		Tuple:
         self.assertTrue(np.all(np.mean(_A       ,					axis=0     ) < 1E-3     )     )
         self.assertTrue(np.all(np.abs(np.var(_A       ,					axis=0     ) - 1     ) < 1E-3     )     )





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

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

         # Test batched
         snake_case				  =      feat_extract(_A       ,					return_tensors="np"     ).input_values
         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     )     )

         # Test 2-D numpy arrays are batched.
         snake_case				  =      [floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         snake_case				  =      np.asarray(_A     )
         snake_case				  =      feat_extract(_A       ,					return_tensors="np"     ).input_values
         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     )     )





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

         snake_case				  =      ["longest", "max_length", "do_not_pad"]
         snake_case				  =      [None, 1_6_0_0, None]
         for max_length, padding in zip(_A       ,					_A     ):
             snake_case				  =      feat_extract(_A       ,					padding=_A       ,					max_length=_A       ,					return_tensors="np"     )
             snake_case				  =      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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
         snake_case				  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
         snake_case				  =      range(8_0_0       ,					1_4_0_0       ,					2_0_0     )
         snake_case				  =      [floats_list((1, x)     )[0] for x in lengths]

         snake_case				  =      ["longest", "max_length", "do_not_pad"]
         snake_case				  =      [None, 1_6_0_0, None]

         for max_length, padding in zip(_A       ,					_A     ):
             snake_case				  =      feat_extract(_A       ,					max_length=_A       ,					padding=_A     )
             snake_case				  =      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 	UpperCAmelCase(self   :					int     )		->		Union[str, Any]:
         snake_case				  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
         snake_case				  =      [floats_list((1, x)     )[0] for x in range(8_0_0       ,					1_4_0_0       ,					2_0_0     )]
         snake_case				  =      feat_extract(
             _A       ,					truncation=_A       ,					max_length=1_0_0_0       ,					padding="max_length"       ,					return_tensors="np"     )
         snake_case				  =      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 	UpperCAmelCase(self   :					List[str]     )		->		int:
         snake_case				  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
         snake_case				  =      [floats_list((1, x)     )[0] for x in range(8_0_0       ,					1_4_0_0       ,					2_0_0     )]
         snake_case				  =      feat_extract(
             _A       ,					truncation=_A       ,					max_length=1_0_0_0       ,					padding="longest"       ,					return_tensors="np"     )
         snake_case				  =      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				  =      [floats_list((1, x)     )[0] for x in range(8_0_0       ,					1_4_0_0       ,					2_0_0     )]
         snake_case				  =      feat_extract(
             _A       ,					truncation=_A       ,					max_length=2_0_0_0       ,					padding="longest"       ,					return_tensors="np"     )
         snake_case				  =      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)     )





     @require_torch
     def 	UpperCAmelCase(self   :					Dict     )		->		Any:
         import torch

         snake_case				  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
         snake_case				  =      np.random.rand(1_0_0     ).astype(np.floataa     )
         snake_case				  =      np_speech_inputs.tolist()

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





     @slow
     @require_torch
     def 	UpperCAmelCase(self   :					List[str]     )		->		str:
         # this test makes sure that models that are using
         # group norm don't have their feature extractor return the
         # attention_mask
         for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             snake_case				  =      WavaVecaConfig.from_pretrained(_A     )
             snake_case				  =      WavaVecaFeatureExtractor.from_pretrained(_A     )

             # only "layer" feature extraction norm should make use of
             # attention_mask
             self.assertEqual(feat_extract.return_attention_mask       ,					config.feat_extract_norm == "layer"     )



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Dict     )		->		Dict:
         snake_case				  =      inspect.getfile(accelerate.test_utils     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
         snake_case				  =      os.path.sep.join(
             mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
         print(f'Command: {cmd}'     )
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,					cuda_visible_devices="0,1"     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )


if __name__ == "__main__":
   _A						      =				Accelerator()
   _A						      =				(accelerator.state.process_index + 2, 10)
   _A						      =				torch.randint(0, 10, shape).to(accelerator.device)

   _A						      =				""

   _A						      =				accelerator.pad_across_processes(tensor)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
      error_msg += "Padding was not done with the right value (0)."

   _A						      =				accelerator.pad_across_processes(tensor, pad_first=True)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   _A						      =				accelerator.state.num_processes - accelerator.state.process_index - 1
   if not torch.equal(tensora[index:], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[:index] == 0):
      error_msg += "Padding was not done with the right value (0)."

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)

