






"""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       __A		(   SCREAMING_SNAKE_CASE_			):
					# to overwrite at feature extractactor specific tests
					UpperCAmelCase__								=				None
					UpperCAmelCase__								=				None

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											return self.feat_extract_tester.prepare_feat_extract_dict()

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Any:
											__magic_name__:	Dict							=       self.feature_extraction_class(**self.feat_extract_dict					)
											self.assertTrue(hasattr(__snake_case     ,		"""feature_size"""					)					)
											self.assertTrue(hasattr(__snake_case     ,		"""sampling_rate"""					)					)
											self.assertTrue(hasattr(__snake_case     ,		"""padding_value"""					)					)

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	List[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	List[str]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	str							=       feat_extract.model_input_names[0]

											__magic_name__:	Any							=       BatchFeature({input_name: speech_inputs}					)

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

											__magic_name__:	List[Any]							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	str							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""np"""					)

											__magic_name__:	List[str]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	Optional[int]							=       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  :      Tuple					)			->							List[Any]:
											__magic_name__:	Tuple							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	Optional[int]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Dict							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[int]							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""pt"""					)

											__magic_name__:	Union[str, Any]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	int							=       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]					)			->							Optional[int]:
											__magic_name__:	Dict							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	Optional[int]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Optional[Any]							=       feat_extract.model_input_names[0]

											__magic_name__:	Tuple							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""tf"""					)

											__magic_name__:	Optional[int]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	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  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=False					)			->							Any:
											def _inputs_have_equal_length(__snake_case  :      int					):
																	__magic_name__:	Union[str, Any]							=       len(input[0]					)
																	for input_slice in input[1:]:
																							if len(__snake_case					) != length:
																													return False
																	return True

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

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

											__magic_name__:	str							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Any							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case					)
											__magic_name__:	Tuple							=       feat_extract.model_input_names[0]

											__magic_name__:	Tuple							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	List[str]							=       self.feat_extract_tester.seq_length_diff
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.max_seq_length + pad_diff
											__magic_name__:	Tuple							=       self.feat_extract_tester.min_seq_length
											__magic_name__:	List[str]							=       self.feat_extract_tester.batch_size
											__magic_name__:	int							=       self.feat_extract_tester.feature_size

											# test padding for List[int] + numpy
											__magic_name__:	Union[str, Any]							=       feat_extract.pad(__snake_case     ,		padding=__snake_case					)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											__magic_name__:	str							=       feat_extract.pad(__snake_case     ,		padding="""longest"""					)
											__magic_name__:	str							=       input_a[input_name]

											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[-1]					)					)
											__magic_name__:	Any							=       input_a[input_name]

											__magic_name__:	str							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)
											__magic_name__:	Any							=       input_a[input_name]

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

											__magic_name__:	Union[str, Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=__snake_case     ,		return_tensors="""np"""					)
											__magic_name__:	Tuple							=       input_a[input_name]

											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_are_equal(__snake_case     ,		__snake_case					)					)
											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
											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		pad_to_multiple_of=1_0					)
											__magic_name__:	int							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		pad_to_multiple_of=1_0					)
											__magic_name__:	List[Any]							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		pad_to_multiple_of=1_0     ,		max_length=__snake_case					)
											__magic_name__:	int							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		pad_to_multiple_of=1_0     ,		max_length=__snake_case     ,		return_tensors="""np"""     ,		)
											__magic_name__:	List[Any]							=       input_a[input_name]

											self.assertTrue(all(len(__snake_case					) % 1_0 == 0 for x in input_a					)					)
											self.assertTrue(_inputs_are_equal(__snake_case     ,		__snake_case					)					)

											__magic_name__:	Tuple							=       pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
											self.assertTrue(all(len(__snake_case					) == 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
											__magic_name__:	Optional[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  :      Any     ,		__snake_case  :      Optional[int]=False					)			->							Optional[Any]:
											def _inputs_have_equal_length(__snake_case  :      Union[str, Any]					):
																	__magic_name__:	Tuple							=       len(input[0]					)
																	for input_slice in input[1:]:
																							if len(__snake_case					) != length:
																													return False
																	return True

											def _inputs_are_equal(__snake_case  :      List[str]     ,		__snake_case  :      Optional[int]					):
																	if len(__snake_case					) != len(__snake_case					):
																							return False

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

											__magic_name__:	List[Any]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	List[str]							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case					)
											__magic_name__:	str							=       feat_extract.model_input_names[0]

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

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

											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)					)
											__magic_name__:	Dict							=       input_a[input_name]

											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)

											# truncate to smallest with np
											__magic_name__:	Union[str, Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		return_tensors="""np"""     ,		truncation=__snake_case     ,		)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											__magic_name__:	Dict							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		return_tensors="""np"""					)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											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(__snake_case					)					)

											# truncate to middle
											__magic_name__:	List[str]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		truncation=__snake_case     ,		return_tensors="""np"""     ,		)
											__magic_name__:	Tuple							=       input_a[input_name]

											__magic_name__:	Optional[int]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		truncation=__snake_case					)
											__magic_name__:	Any							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		return_tensors="""np"""					)
											__magic_name__:	Tuple							=       input_a[input_name]

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

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

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

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

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

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

											# test truncation for `pad_to_multiple_of` for List[int] + numpy
											__magic_name__:	Tuple							=       1_2
											__magic_name__:	Optional[Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		pad_to_multiple_of=__snake_case     ,		truncation=__snake_case     ,		)
											__magic_name__:	Tuple							=       input_a[input_name]

											__magic_name__:	List[str]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		pad_to_multiple_of=__snake_case     ,		)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

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

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[int]:
											self._check_padding(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											self._check_padding(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											self._check_truncation(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      int					)			->							int:
											self._check_truncation(numpify=__snake_case					)

					@require_torch
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	List[Any]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	Optional[int]							=       feat_extract.model_input_names[0]

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

											__magic_name__:	List[Any]							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)[input_name]
											__magic_name__:	Any							=       feat_extract.pad(__snake_case     ,		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  :      Union[str, Any]					)			->							int:
											__magic_name__:	Dict							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	str							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	Any							=       feat_extract.model_input_names[0]

											__magic_name__:	Union[str, Any]							=       BatchFeature({input_name: speech_inputs}					)

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

											__magic_name__:	List[str]							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	Tuple							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)
											self.assertIn("""attention_mask"""     ,		__snake_case					)
											self.assertListEqual(list(processed.attention_mask.shape					)     ,		list(processed[input_name].shape[:2]					)					)
											self.assertListEqual(processed.attention_mask.sum(-1					).tolist()     ,		__snake_case					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Tuple:
											__magic_name__:	Any							=       self.feat_extract_dict
											__magic_name__:	Optional[Any]							=       True
											__magic_name__:	str							=       self.feature_extraction_class(**__snake_case					)
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	str							=       [len(__snake_case					) for x in speech_inputs]
											__magic_name__:	Union[str, Any]							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[int]							=       BatchFeature({input_name: speech_inputs}					)
											__magic_name__:	Union[str, Any]							=       min(__snake_case					)

											__magic_name__:	Dict							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=__snake_case     ,		truncation=__snake_case     ,		return_tensors="""np"""					)
											self.assertIn("""attention_mask"""     ,		__snake_case					)
											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]					)










from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   BlenderbotSmallConfig
      UpperCAmelCase__							=   {}
      UpperCAmelCase__							=   """gelu"""


      def __init__(		self   ,				a__   ,				a__=13   ,				a__=7   ,				a__=True   ,				a__=False   ,				a__=99   ,				a__=32   ,				a__=2   ,				a__=4   ,				a__=37   ,				a__=0.1   ,				a__=0.1   ,				a__=20   ,				a__=2   ,				a__=1   ,				a__=0   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		parent
             _lowerCamelCase				:  Any						=		batch_size
             _lowerCamelCase				:  str						=		seq_length
             _lowerCamelCase				:  Optional[int]						=		is_training
             _lowerCamelCase				:  int						=		use_labels
             _lowerCamelCase				:  List[str]						=		vocab_size
             _lowerCamelCase				:  List[Any]						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  int						=		intermediate_size

             _lowerCamelCase				:  int						=		hidden_dropout_prob
             _lowerCamelCase				:  Tuple						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Dict						=		max_position_embeddings
             _lowerCamelCase				:  str						=		eos_token_id
             _lowerCamelCase				:  Any						=		pad_token_id
             _lowerCamelCase				:  str						=		bos_token_id


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size)
             _lowerCamelCase				:  Any						=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)   ,				1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([input_ids, eos_tensor]   ,				axis=1)

             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size)

             _lowerCamelCase				:  Dict						=		self.config_cls(
                 vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
             _lowerCamelCase				:  Optional[int]						=		prepare_blenderbot_small_inputs_dict(a__   ,				a__   ,				a__)
             return config, inputs_dict



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		TFBlenderbotSmallModel(config=a__).get_decoder()
             _lowerCamelCase				:  Dict						=		inputs_dict['''input_ids''']

             _lowerCamelCase				:  int						=		input_ids[:1, :]
             _lowerCamelCase				:  Union[str, Any]						=		inputs_dict['''attention_mask'''][:1, :]
             _lowerCamelCase				:  List[str]						=		inputs_dict['''head_mask''']
             _lowerCamelCase				:  Optional[int]						=		1

             # first forward pass
             _lowerCamelCase				:  Union[str, Any]						=		model(a__   ,				attention_mask=a__   ,				head_mask=a__   ,				use_cache=a__)

             _lowerCamelCase,	_lowerCamelCase				:  str						=		outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _lowerCamelCase				:  Any						=		ids_tensor((self.batch_size, 3)   ,				config.vocab_size)
             _lowerCamelCase				:  Optional[Any]						=		tf.cast(ids_tensor((self.batch_size, 3)   ,				2)   ,				tf.inta)

             # append to next input_ids and
             _lowerCamelCase				:  Any						=		tf.concat([input_ids, next_tokens]   ,				axis=-1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([attention_mask, next_attn_mask]   ,				axis=-1)

             _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__)[0]
             _lowerCamelCase				:  str						=		model(a__   ,				attention_mask=a__   ,				past_key_values=a__)[0]

             self.parent.assertEqual(next_tokens.shape[1]   ,				output_from_past.shape[1])

             # select random slice
             _lowerCamelCase				:  int						=		int(ids_tensor((1,)   ,				output_from_past.shape[-1]))
             _lowerCamelCase				:  Any						=		output_from_no_past[:, -3:, random_slice_idx]
             _lowerCamelCase				:  int						=		output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(a__   ,				a__   ,				rtol=1e-3)




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     ):
       if attention_mask is None:
              _lowerCamelCase				:  List[Any]						=		tf.cast(tf.math.not_equal(lowercase_			,     config.pad_token_id   )			,     tf.inta   )
       if decoder_attention_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape			,     dtype=tf.inta   ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,     config.pad_token_id   )			,     tf.inta   ),
                  ]			,     axis=-1			,     )
       if head_mask is None:
              _lowerCamelCase				:  List[str]						=		tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _lowerCamelCase				:  Optional[int]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }







@require_tf
class       __A							(   lowerCamelCase__			,lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      UpperCAmelCase__							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      UpperCAmelCase__							=   (
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		TFBlenderbotSmallModelTester(self)
             _lowerCamelCase				:  Optional[int]						=		ConfigTester(self   ,				config_class=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.config_tester.run_common_tests()



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_tokenizers
@require_tf
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   [
          """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
          """  i'm going to throw up.\nand why is that?"""
      ]
      UpperCAmelCase__							=   """facebook/blenderbot_small-90M"""


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.tokenizer(self.src_text   ,				return_tensors='''tf''')
             _lowerCamelCase				:  str						=		self.model.generate(
                 model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=a__   ,				)
             _lowerCamelCase				:  Tuple						=		self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=a__)[0]
             assert generated_words in (
                 "i don't know. i just feel like i'm going to throw up. it's not fun.",
                 "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                 "i'm not sure. i just feel like i've been in a bad situation.",
             )

