






"""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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					None
	_A							:					Optional[Any] 			=					None





	@property
	def  lowerCamelCase(self				):
		return self.feat_extract_tester.prepare_feat_extract_dict()





	def  lowerCamelCase(self				):
		A_		:    int						     = self.feature_extraction_class(**self.feat_extract_dict				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """feature_size"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """sampling_rate"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """padding_value"""				)				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.feat_extract_tester.prepare_inputs_for_common()
		A_		:    List[Any]						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    Tuple						     = feat_extract.model_input_names[0]

		A_		:    int						     = BatchFeature({input_name: speech_inputs}				)

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

		A_		:    int						     = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_				)
		A_		:    Any						     = BatchFeature({input_name: speech_inputs}  ,  tensor_type="""np"""				)

		A_		:    Tuple						     = processed_features[input_name]

		if len(batch_features_input.shape				) < 3:
			A_		:    Union[str, Any]						     = 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				):
		A_		:    str						     = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_				)
		A_		:    str						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    Tuple						     = feat_extract.model_input_names[0]

		A_		:    List[Any]						     = BatchFeature({input_name: speech_inputs}  ,  tensor_type="""pt"""				)

		A_		:    Any						     = processed_features[input_name]

		if len(batch_features_input.shape				) < 3:
			A_		:    Dict						     = 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				):
		A_		:    Any						     = self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    Optional[Any]						     = feat_extract.model_input_names[0]

		A_		:    Dict						     = BatchFeature({input_name: speech_inputs}  ,  tensor_type="""tf"""				)

		A_		:    List[str]						     = processed_features[input_name]

		if len(batch_features_input.shape				) < 3:
			A_		:    Union[str, Any]						     = 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  ,  lowerCAmelCase_=False				):
		def _inputs_have_equal_length(lowerCAmelCase_				):
			A_		:    str						     = len(input[0]				)
			for input_slice in input[1:]:
				if len(lowerCAmelCase_				) != length:
					return False
			return True

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

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

		A_		:    Union[str, Any]						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    Any						     = self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_				)
		A_		:    Dict						     = feat_extract.model_input_names[0]

		A_		:    Optional[int]						     = BatchFeature({input_name: speech_inputs}				)

		A_		:    Dict						     = self.feat_extract_tester.seq_length_diff
		A_		:    List[Any]						     = self.feat_extract_tester.max_seq_length + pad_diff
		A_		:    List[str]						     = self.feat_extract_tester.min_seq_length
		A_		:    Tuple						     = self.feat_extract_tester.batch_size
		A_		:    Dict						     = self.feat_extract_tester.feature_size

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

		A_		:    Optional[Any]						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""longest"""				)
		A_		:    Any						     = input_a[input_name]

		A_		:    Any						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[-1]				)				)
		A_		:    Tuple						     = input_a[input_name]

		A_		:    Optional[Any]						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""longest"""  ,  return_tensors="""np"""				)
		A_		:    str						     = input_a[input_name]

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

		A_		:    Union[str, Any]						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=lowerCAmelCase_  ,  return_tensors="""np"""				)
		A_		:    Dict						     = input_a[input_name]

		self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_				)				)
		self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_				)				)
		self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_				)				)
		self.assertTrue(_inputs_are_equal(lowerCAmelCase_  ,  lowerCAmelCase_				)				)
		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_		:    Dict						     = feat_extract.pad(lowerCAmelCase_  ,  pad_to_multiple_of=10				)
		A_		:    Dict						     = input_a[input_name]

		A_		:    List[Any]						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""longest"""  ,  pad_to_multiple_of=10				)
		A_		:    Optional[Any]						     = input_a[input_name]

		A_		:    Dict						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  pad_to_multiple_of=10  ,  max_length=lowerCAmelCase_				)
		A_		:    List[str]						     = input_a[input_name]

		A_		:    str						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  pad_to_multiple_of=10  ,  max_length=lowerCAmelCase_  ,  return_tensors="""np"""  ,  )
		A_		:    Tuple						     = input_a[input_name]

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

		A_		:    Any						     = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
		self.assertTrue(all(len(lowerCAmelCase_				) == 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_		:    int						     = (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  ,  lowerCAmelCase_=False				):
		def _inputs_have_equal_length(lowerCAmelCase_				):
			A_		:    Tuple						     = len(input[0]				)
			for input_slice in input[1:]:
				if len(lowerCAmelCase_				) != length:
					return False
			return True

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

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

		A_		:    Tuple						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    List[str]						     = self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase_				)
		A_		:    Dict						     = feat_extract.model_input_names[0]

		A_		:    Dict						     = BatchFeature({input_name: speech_inputs}				)

		# truncate to smallest
		A_		:    Any						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[0]				)  ,  truncation=lowerCAmelCase_				)
		A_		:    int						     = input_a[input_name]

		A_		:    Optional[int]						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[0]				)				)
		A_		:    str						     = input_a[input_name]

		self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_				)				)
		self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_				)				)

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

		A_		:    int						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[0]				)  ,  return_tensors="""np"""				)
		A_		:    Any						     = input_a[input_name]

		self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_				)				)
		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(lowerCAmelCase_				)				)

		# truncate to middle
		A_		:    str						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[1]				)  ,  truncation=lowerCAmelCase_  ,  return_tensors="""np"""  ,  )
		A_		:    Union[str, Any]						     = input_a[input_name]

		A_		:    str						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[1]				)  ,  truncation=lowerCAmelCase_				)
		A_		:    List[Any]						     = input_a[input_name]

		A_		:    Any						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[1]				)  ,  return_tensors="""np"""				)
		A_		:    List[Any]						     = input_a[input_name]

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

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

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

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

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

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

		# test truncation for `pad_to_multiple_of` for List[int] + numpy
		A_		:    List[str]						     = 12
		A_		:    Dict						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[0]				)  ,  pad_to_multiple_of=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  )
		A_		:    List[Any]						     = input_a[input_name]

		A_		:    Any						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=len(speech_inputs[0]				)  ,  pad_to_multiple_of=lowerCAmelCase_  ,  )
		A_		:    Optional[int]						     = input_a[input_name]

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





	def  lowerCamelCase(self				):
		self._check_padding(numpify=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self._check_padding(numpify=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self._check_truncation(numpify=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self._check_truncation(numpify=lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    Dict						     = self.feat_extract_tester.prepare_inputs_for_common()
		A_		:    Any						     = feat_extract.model_input_names[0]

		A_		:    Optional[int]						     = BatchFeature({input_name: speech_inputs}				)

		A_		:    int						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""longest"""  ,  return_tensors="""np"""				)[input_name]
		A_		:    Any						     = feat_extract.pad(lowerCAmelCase_  ,  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				):
		A_		:    int						     = self.feature_extraction_class(**self.feat_extract_dict				)
		A_		:    List[Any]						     = self.feat_extract_tester.prepare_inputs_for_common()
		A_		:    Optional[int]						     = feat_extract.model_input_names[0]

		A_		:    Optional[Any]						     = BatchFeature({input_name: speech_inputs}				)

		A_		:    Dict						     = feat_extract.pad(lowerCAmelCase_  ,  padding="""longest"""  ,  return_tensors="""np"""				)[input_name]
		A_		:    Any						     = feat_extract.pad(lowerCAmelCase_  ,  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				):
		A_		:    int						     = self.feat_extract_dict
		A_		:    Optional[int]						     = True
		A_		:    Union[str, Any]						     = self.feature_extraction_class(**lowerCAmelCase_				)
		A_		:    Tuple						     = self.feat_extract_tester.prepare_inputs_for_common()
		A_		:    List[Any]						     = [len(lowerCAmelCase_				) for x in speech_inputs]
		A_		:    Union[str, Any]						     = feat_extract.model_input_names[0]

		A_		:    Dict						     = BatchFeature({input_name: speech_inputs}				)

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





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.feat_extract_dict
		A_		:    Optional[Any]						     = True
		A_		:    Union[str, Any]						     = self.feature_extraction_class(**lowerCAmelCase_				)
		A_		:    str						     = self.feat_extract_tester.prepare_inputs_for_common()
		A_		:    int						     = [len(lowerCAmelCase_				) for x in speech_inputs]
		A_		:    Union[str, Any]						     = feat_extract.model_input_names[0]

		A_		:    Optional[int]						     = BatchFeature({input_name: speech_inputs}				)
		A_		:    List[Any]						     = min(lowerCAmelCase_				)

		A_		:    Optional[int]						     = feat_extract.pad(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  return_tensors="""np"""				)
		self.assertIn("""attention_mask"""  ,  lowerCAmelCase_				)
		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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TimesformerModel",
							    "TimesformerForVideoClassification",
							    "TimesformerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

