




"""simple docstring"""

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 # to overwrite at feature extractactor specific tests
 UpperCAmelCase   :					Dict				  =     None
 UpperCAmelCase   :					Optional[Any]				  =     None

 @property
 def       lowerCAmelCase_							(			self	:	int		):
       return self.feat_extract_tester.prepare_feat_extract_dict()

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'feature_size'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'sampling_rate'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'padding_value'		)		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.feat_extract_tester.prepare_inputs_for_common()
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

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

       _A        =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase		)
       _A        =       BatchFeature({input_name: speech_inputs}   ,							tensor_type='np'		)

       _A        =       processed_features[input_name]

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

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

 @require_torch
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase		)
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}   ,							tensor_type='pt'		)

       _A        =       processed_features[input_name]

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

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

 @require_tf
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase		)
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}   ,							tensor_type='tf'		)

       _A        =       processed_features[input_name]

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

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Optional[Any]=False		):
       def _inputs_have_equal_length(_UpperCAmelCase	:	Optional[Any]		):
             _A        =       len(input[0]		)
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase		) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Dict		):
             if len(_UpperCAmelCase		) != len(_UpperCAmelCase		):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   if not np.allclose(np.asarray(_UpperCAmelCase		)   ,							np.asarray(_UpperCAmelCase		)   ,							atol=1E-3		):
                         return False
             return True

       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase		)
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

       _A        =       self.feat_extract_tester.seq_length_diff
       _A        =       self.feat_extract_tester.max_seq_length + pad_diff
       _A        =       self.feat_extract_tester.min_seq_length
       _A        =       self.feat_extract_tester.batch_size
       _A        =       self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding=_UpperCAmelCase		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[-1]		)		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							return_tensors='np'		)
       _A        =       input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(_UpperCAmelCase		):
             feat_extract.pad(_UpperCAmelCase   ,							padding='max_length'		)[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=_UpperCAmelCase   ,							return_tensors='np'		)
       _A        =       input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase   ,							_UpperCAmelCase		)		)
       self.assertTrue(len(input_a[0]		) == pad_min_length		)
       self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       _A        =       feat_extract.pad(_UpperCAmelCase   ,							pad_to_multiple_of=10		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							pad_to_multiple_of=10		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							pad_to_multiple_of=10   ,							max_length=_UpperCAmelCase		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							pad_to_multiple_of=10   ,							max_length=_UpperCAmelCase   ,							return_tensors='np'   ,							)
       _A        =       input_a[input_name]

       self.assertTrue(all(len(_UpperCAmelCase		) % 10 == 0 for x in input_a		)		)
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

       _A        =       pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(_UpperCAmelCase		) == expected_mult_pad_length for x in input_a		)		)
       self.assertEqual(input_a.shape[:2]   ,							(batch_size, expected_mult_pad_length)		)

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size		)

       # Check padding value is correct
       _A        =       (np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
           < 1E-3		)
       self.assertTrue(
           abs(
               np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
           < 1E-3		)
       self.assertTrue(
           abs(
               np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
           < 1E-3		)
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1E-3		)
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
           < 1E-3		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	str=False		):
       def _inputs_have_equal_length(_UpperCAmelCase	:	Dict		):
             _A        =       len(input[0]		)
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase		) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	str		):
             if len(_UpperCAmelCase		) != len(_UpperCAmelCase		):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   if not np.allclose(np.asarray(_UpperCAmelCase		)   ,							np.asarray(_UpperCAmelCase		)   ,							atol=1E-3		):
                         return False
             return True

       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase		)
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

       # truncate to smallest
       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)   ,							truncation=_UpperCAmelCase		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)		)
       _A        =       input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase		)		)

       # truncate to smallest with np
       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)   ,							return_tensors='np'   ,							truncation=_UpperCAmelCase   ,							)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)   ,							return_tensors='np'		)
       _A        =       input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase		)		)

       # truncate to middle
       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[1]		)   ,							truncation=_UpperCAmelCase   ,							return_tensors='np'   ,							)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[1]		)   ,							truncation=_UpperCAmelCase		)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[1]		)   ,							return_tensors='np'		)
       _A        =       input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase		):
             feat_extract.pad(_UpperCAmelCase   ,							truncation=_UpperCAmelCase		)[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase		):
             feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							truncation=_UpperCAmelCase		)[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase		):
             feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							truncation=_UpperCAmelCase		)[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(_UpperCAmelCase		):
             feat_extract.pad(_UpperCAmelCase   ,							padding='max_length'   ,							truncation=_UpperCAmelCase		)[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       _A        =       12
       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)   ,							pad_to_multiple_of=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							)
       _A        =       input_a[input_name]

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=len(speech_inputs[0]		)   ,							pad_to_multiple_of=_UpperCAmelCase   ,							)
       _A        =       input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       _A        =       len(speech_inputs[0]		)
       if expected_length % pad_to_multiple_of != 0:
             _A        =       ((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]		) == expected_length		)
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase		)		)
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase		)		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       self._check_padding(numpify=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self._check_padding(numpify=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       self._check_truncation(numpify=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       self._check_truncation(numpify=_UpperCAmelCase		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common()
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							return_tensors='np'		)[input_name]
       _A        =       feat_extract.pad(_UpperCAmelCase   ,							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		)

 @require_tf
 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       self.feature_extraction_class(**self.feat_extract_dict		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common()
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							return_tensors='np'		)[input_name]
       _A        =       feat_extract.pad(_UpperCAmelCase   ,							padding='longest'   ,							return_tensors='tf'		)[input_name]

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

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self.feat_extract_dict
       _A        =       True
       _A        =       self.feature_extraction_class(**_UpperCAmelCase		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common()
       _A        =       [len(_UpperCAmelCase		) for x in speech_inputs]
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)

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



 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       self.feat_extract_dict
       _A        =       True
       _A        =       self.feature_extraction_class(**_UpperCAmelCase		)
       _A        =       self.feat_extract_tester.prepare_inputs_for_common()
       _A        =       [len(_UpperCAmelCase		) for x in speech_inputs]
       _A        =       feat_extract.model_input_names[0]

       _A        =       BatchFeature({input_name: speech_inputs}		)
       _A        =       min(_UpperCAmelCase		)

       _A        =       feat_extract.pad(
           _UpperCAmelCase   ,							padding='max_length'   ,							max_length=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='np'		)
       self.assertIn('attention_mask'   ,							_UpperCAmelCase		)
       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]		)







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

