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 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					None
 __A			       =					None







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	int:


   """simple docstring"""







   return self.feat_extract_tester.prepare_feat_extract_dict()







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


   """simple docstring"""







   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   Tuple)						->	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(A_) == len(A_) for x, y in zip(A_							, processed_features[input_name])))

   _UpperCamelCase           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
   _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				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
   _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				:   int)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
   _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				:   Dict							, lowercase_				:   int=False)						->	int:


   """simple docstring"""







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

   def _inputs_are_equal(lowercase_				:   List[Any]							, lowercase_				:   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

   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_dict)
   _UpperCamelCase           =    self.feat_extract_tester.prepare_inputs_for_common(numpify=A_)
   _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(A_							, padding=A_)
   _UpperCamelCase           =    input_a[input_name]

   _UpperCamelCase           =    feat_extract.pad(A_							, padding="longest")
   _UpperCamelCase           =    input_a[input_name]

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

   _UpperCamelCase           =    feat_extract.pad(A_							, 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(A_):
     feat_extract.pad(A_							, padding="max_length")[input_name]

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

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

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

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

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

   _UpperCamelCase           =    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
   _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				:   Any							, lowercase_				:   Union[str, Any]=False)						->	Any:


   """simple docstring"""







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

   def _inputs_are_equal(lowercase_				:   int							, lowercase_				:   List[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

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

   _UpperCamelCase           =    BatchFeature({input_name: speech_inputs})

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

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

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

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

   _UpperCamelCase           =    feat_extract.pad(
       A_							, padding="max_length"							, max_length=len(speech_inputs[0])							, return_tensors="np")
   _UpperCamelCase           =    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
   _UpperCamelCase           =    feat_extract.pad(
       A_							, padding="max_length"							, max_length=len(speech_inputs[1])							, truncation=A_							, return_tensors="np"							, )
   _UpperCamelCase           =    input_a[input_name]

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

   _UpperCamelCase           =    feat_extract.pad(
       A_							, 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(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
   _UpperCamelCase           =    12
   _UpperCamelCase           =    feat_extract.pad(
       A_							, padding="max_length"							, max_length=len(speech_inputs[0])							, pad_to_multiple_of=A_							, truncation=A_							, )
   _UpperCamelCase           =    input_a[input_name]

   _UpperCamelCase           =    feat_extract.pad(
       A_							, padding="max_length"							, max_length=len(speech_inputs[0])							, pad_to_multiple_of=A_							, )
   _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(A_))
   self.assertFalse(_inputs_have_equal_length(A_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Any:


   """simple docstring"""







   self._check_padding(numpify=A_)







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


   """simple docstring"""







   self._check_padding(numpify=A_)







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Dict:


   """simple docstring"""







   self._check_truncation(numpify=A_)







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


   """simple docstring"""







   self._check_truncation(numpify=A_)







 @require_torch
 def 		__UpperCAmelCase				(		self				:   Dict)						->	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(A_							, padding="longest"							, return_tensors="np")[input_name]
   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Union[str, Any]:


   """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(A_							, padding="longest"							, return_tensors="np")[input_name]
   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







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

   _UpperCamelCase           =    BatchFeature({input_name: speech_inputs})

   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   List[str])						->	Optional[int]:


   """simple docstring"""







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

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

   _UpperCamelCase           =    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])








from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''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							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

       _UpperCAmelCase		:							Union[str, Any]							=     hidden_dropout_prob
       _UpperCAmelCase		:							str							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[Any]							=     eos_token_id
       _UpperCAmelCase		:							Optional[Any]							=     pad_token_id
       _UpperCAmelCase		:							Tuple							=     bos_token_id


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

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[Any]							=     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							,					)
       _UpperCAmelCase		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

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

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

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     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
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

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




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							str							=     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:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     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 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=A_    )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )





