
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       ):
		# to overwrite at feature extractactor specific tests
		__lowercase	:	Any     					=    None
		__lowercase	:	Union[str, Any]     					=    None



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
			"""simple docstring"""
			return self.feat_extract_tester.prepare_feat_extract_dict()



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
			"""simple docstring"""
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			self.assertTrue(hasattr(_lowercase    ,				'feature_size'	)	)
			self.assertTrue(hasattr(_lowercase    ,				'sampling_rate'	)	)
			self.assertTrue(hasattr(_lowercase    ,				'padding_value'	)	)



		def __UpperCamelCase			(			self	)  -> Tuple:
			"""simple docstring"""
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common()
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			UpperCamelCase          =					feat_extract.model_input_names[0]

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

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

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

			UpperCamelCase          =					processed_features[input_name]

			if len(batch_features_input.shape	) < 3:
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
			"""simple docstring"""
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowercase	)
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			UpperCamelCase          =					feat_extract.model_input_names[0]

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

			UpperCamelCase          =					processed_features[input_name]

			if len(batch_features_input.shape	) < 3:
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> List[str]:
			"""simple docstring"""
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowercase	)
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			UpperCamelCase          =					feat_extract.model_input_names[0]

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

			UpperCamelCase          =					processed_features[input_name]

			if len(batch_features_input.shape	) < 3:
				UpperCamelCase          =					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 __UpperCamelCase			(			self    ,				A_=False	)  -> Optional[Any]:
			"""simple docstring"""
			def _inputs_have_equal_length(A_	):
				UpperCamelCase          =					len(input[0]	)
				for input_slice in input[1:]:
					if len(_lowercase	) != length:
						return False
				return True

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

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

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

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

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

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

			UpperCamelCase          =					feat_extract.pad(_lowercase    ,				padding='longest'	)
			UpperCamelCase          =					input_a[input_name]

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

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

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

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

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

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

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

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

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

			UpperCamelCase          =					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
			self.assertTrue(all(len(_lowercase	) == 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
			UpperCamelCase          =					(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 __UpperCamelCase			(			self    ,				A_=False	)  -> Optional[int]:
			"""simple docstring"""
			def _inputs_have_equal_length(A_	):
				UpperCamelCase          =					len(input[0]	)
				for input_slice in input[1:]:
					if len(_lowercase	) != length:
						return False
				return True

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

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

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

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

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

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

			self.assertTrue(_inputs_have_equal_length(_lowercase	)	)
			self.assertFalse(_inputs_have_equal_length(_lowercase	)	)

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

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

			self.assertTrue(_inputs_have_equal_length(_lowercase	)	)
			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(_lowercase	)	)

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

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

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

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

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

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

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

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

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

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

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

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



		def __UpperCamelCase			(			self	)  -> Tuple:
			"""simple docstring"""
			self._check_padding(numpify=_lowercase	)



		def __UpperCamelCase			(			self	)  -> int:
			"""simple docstring"""
			self._check_padding(numpify=_lowercase	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
			"""simple docstring"""
			self._check_truncation(numpify=_lowercase	)



		def __UpperCamelCase			(			self	)  -> str:
			"""simple docstring"""
			self._check_truncation(numpify=_lowercase	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> str:
			"""simple docstring"""
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common()
			UpperCamelCase          =					feat_extract.model_input_names[0]

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

			UpperCamelCase          =					feat_extract.pad(_lowercase    ,				padding='longest'    ,				return_tensors='np'	)[input_name]
			UpperCamelCase          =					feat_extract.pad(_lowercase    ,				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 __UpperCamelCase			(			self	)  -> int:
			"""simple docstring"""
			UpperCamelCase          =					self.feature_extraction_class(**self.feat_extract_dict	)
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common()
			UpperCamelCase          =					feat_extract.model_input_names[0]

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

			UpperCamelCase          =					feat_extract.pad(_lowercase    ,				padding='longest'    ,				return_tensors='np'	)[input_name]
			UpperCamelCase          =					feat_extract.pad(_lowercase    ,				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 __UpperCamelCase			(			self	)  -> Optional[int]:
			"""simple docstring"""
			UpperCamelCase          =					self.feat_extract_dict
			UpperCamelCase          =					True
			UpperCamelCase          =					self.feature_extraction_class(**_lowercase	)
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common()
			UpperCamelCase          =					[len(_lowercase	) for x in speech_inputs]
			UpperCamelCase          =					feat_extract.model_input_names[0]

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
			"""simple docstring"""
			UpperCamelCase          =					self.feat_extract_dict
			UpperCamelCase          =					True
			UpperCamelCase          =					self.feature_extraction_class(**_lowercase	)
			UpperCamelCase          =					self.feat_extract_tester.prepare_inputs_for_common()
			UpperCamelCase          =					[len(_lowercase	) for x in speech_inputs]
			UpperCamelCase          =					feat_extract.model_input_names[0]

			UpperCamelCase          =					BatchFeature({input_name: speech_inputs}	)
			UpperCamelCase          =					min(_lowercase	)

			UpperCamelCase          =					feat_extract.pad(
			    _lowercase    ,				padding='max_length'    ,				max_length=_lowercase    ,				truncation=_lowercase    ,				return_tensors='np'	)
			self.assertIn('attention_mask'    ,				_lowercase	)
			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 pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
