





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        SCREAMING_SNAKE_CASE     (_UpperCAmelCase   ):
			_UpperCamelCase					:	List[Any]				      = None
			_UpperCamelCase					:	Optional[Any]				      = None


			@property
			def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Union[str, Any]:





					"""simple docstring"""







					return self.feat_extract_tester.prepare_feat_extract_dict()


			def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[Any]:





					"""simple docstring"""







					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					self.assertTrue(hasattr(A_    ,  'feature_size' ) )
					self.assertTrue(hasattr(A_    ,  'sampling_rate' ) )
					self.assertTrue(hasattr(A_    ,  'padding_value' ) )


			def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[str]:





					"""simple docstring"""







					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common()
					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					lowercase__			    =						feat_extract.model_input_names[0]

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

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

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

					lowercase__			    =						processed_features[input_name]

					if len(batch_features_input.shape ) < 3:
							lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Optional[int]:





					"""simple docstring"""







					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					lowercase__			    =						feat_extract.model_input_names[0]

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

					lowercase__			    =						processed_features[input_name]

					if len(batch_features_input.shape ) < 3:
							lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     List[Any]:





					"""simple docstring"""







					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_ )
					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					lowercase__			    =						feat_extract.model_input_names[0]

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

					lowercase__			    =						processed_features[input_name]

					if len(batch_features_input.shape ) < 3:
							lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       Union[str, Any]=False )->     Optional[Any]:





					"""simple docstring"""







					def _inputs_have_equal_length(a    :       int ):
							lowercase__			    =						len(input[0] )
							for input_slice in input[1:]:
									if len(A_ ) != length:
											return False
							return True

					def _inputs_are_equal(a    :       int    ,  a    :       Optional[int] ):
							if len(A_ ) != len(A_ ):
									return False

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

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

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

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

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

					lowercase__			    =						feat_extract.pad(A_    ,  padding='longest' )
					lowercase__			    =						input_a[input_name]

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

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

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

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

					self.assertFalse(_inputs_have_equal_length(A_ ) )
					self.assertTrue(_inputs_have_equal_length(A_ ) )
					self.assertTrue(_inputs_have_equal_length(A_ ) )
					self.assertTrue(_inputs_are_equal(A_    ,  A_ ) )
					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
					lowercase__			    =						feat_extract.pad(A_    ,  pad_to_multiple_of=10 )
					lowercase__			    =						input_a[input_name]

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

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

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

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

					lowercase__			    =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
					self.assertTrue(all(len(A_ ) == 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
					lowercase__			    =						(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  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       Optional[int]=False )->     Dict:





					"""simple docstring"""







					def _inputs_have_equal_length(a    :       Any ):
							lowercase__			    =						len(input[0] )
							for input_slice in input[1:]:
									if len(A_ ) != length:
											return False
							return True

					def _inputs_are_equal(a    :       str    ,  a    :       str ):
							if len(A_ ) != len(A_ ):
									return False

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

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

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

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

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

					self.assertTrue(_inputs_have_equal_length(A_ ) )
					self.assertFalse(_inputs_have_equal_length(A_ ) )

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

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

					self.assertTrue(_inputs_have_equal_length(A_ ) )
					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(A_ ) )

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

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

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

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

					# 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(A_ ) )
					self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

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

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

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

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

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

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

					# retrieve expected_length as multiple of pad_to_multiple_of
					lowercase__			    =						len(speech_inputs[0] )
					if expected_length % pad_to_multiple_of != 0:
							lowercase__			    =						((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(A_ ) )
					self.assertFalse(_inputs_have_equal_length(A_ ) )


			def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     str:





					"""simple docstring"""







					self._check_padding(numpify=A_ )


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





					"""simple docstring"""







					self._check_padding(numpify=A_ )


			def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Dict:





					"""simple docstring"""







					self._check_truncation(numpify=A_ )


			def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     str:





					"""simple docstring"""







					self._check_truncation(numpify=A_ )


			@require_torch
			def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     List[str]:





					"""simple docstring"""







					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common()
					lowercase__			    =						feat_extract.model_input_names[0]

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

					lowercase__			    =						feat_extract.pad(A_    ,  padding='longest'    ,  return_tensors='np' )[input_name]
					lowercase__			    =						feat_extract.pad(A_    ,  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  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Tuple:





					"""simple docstring"""







					lowercase__			    =						self.feature_extraction_class(**self.feat_extract_dict )
					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common()
					lowercase__			    =						feat_extract.model_input_names[0]

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

					lowercase__			    =						feat_extract.pad(A_    ,  padding='longest'    ,  return_tensors='np' )[input_name]
					lowercase__			    =						feat_extract.pad(A_    ,  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  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[Any]:





					"""simple docstring"""







					lowercase__			    =						self.feat_extract_dict
					lowercase__			    =						True
					lowercase__			    =						self.feature_extraction_class(**A_ )
					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common()
					lowercase__			    =						[len(A_ ) for x in speech_inputs]
					lowercase__			    =						feat_extract.model_input_names[0]

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )

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


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





					"""simple docstring"""







					lowercase__			    =						self.feat_extract_dict
					lowercase__			    =						True
					lowercase__			    =						self.feature_extraction_class(**A_ )
					lowercase__			    =						self.feat_extract_tester.prepare_inputs_for_common()
					lowercase__			    =						[len(A_ ) for x in speech_inputs]
					lowercase__			    =						feat_extract.model_input_names[0]

					lowercase__			    =						BatchFeature({input_name: speech_inputs} )
					lowercase__			    =						min(A_ )

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









import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=7	)       ->     List[Any]:
  lowercase__			    =						None
  if token is not None:
    lowercase__			    =						{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

  # The id of a workflow (not of a workflow run)
  lowercase__			    =						'636036'

  lowercase__			    =						F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

  lowercase__			    =						requests.get(_SCREAMING_SNAKE_CASE	,		headers=_SCREAMING_SNAKE_CASE	).json()

  return result["workflow_runs"]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  lowercase__			    =						get_daily_ci_runs(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      lowercase__			    =						workflow_run['id']
      break

  return workflow_run_id




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						get_last_daily_ci_runs(_SCREAMING_SNAKE_CASE	)
  if workflow_run_id is not None:
    lowercase__			    =						get_artifacts_links(worflow_run_id=_SCREAMING_SNAKE_CASE	,		token=_SCREAMING_SNAKE_CASE	)
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        lowercase__			    =						artifacts_links[artifact_name]
        download_artifact(
            artifact_name=_SCREAMING_SNAKE_CASE	,		artifact_url=_SCREAMING_SNAKE_CASE	,		output_dir=_SCREAMING_SNAKE_CASE	,		token=_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  get_last_daily_ci_artifacts(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						{}
  for artifact_name in artifact_names:
    lowercase__			    =						os.path.join(_SCREAMING_SNAKE_CASE	,		F"""{artifact_name}.zip"""	)
    if os.path.isfile(_SCREAMING_SNAKE_CASE	):
      lowercase__			    =						{}
      with zipfile.ZipFile(_SCREAMING_SNAKE_CASE	) as z:
        for filename in z.namelist():
          if not os.path.isdir(_SCREAMING_SNAKE_CASE	):
            # read the file
            with z.open(_SCREAMING_SNAKE_CASE	) as f:
              lowercase__			    =						f.read().decode('UTF-8'	)

  return results


