




import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  Optional[Any]						,							A_					:  int						,							A_					:  Dict=13						,							A_					:  int=7						,							A_					:  int=True						,							A_					:  int=True						,							A_					:  Any=True						,							A_					:  Union[str, Any]=True						,							A_					:  Dict=99						,							A_					:  List[str]=32						,							A_					:  Tuple=5						,							A_					:  Union[str, Any]=4						,							A_					:  int=37						,							A_					:  Tuple="gelu"						,							A_					:  Union[str, Any]=0.1						,							A_					:  List[str]=0.1						,							A_					:  Optional[Any]=512						,							A_					:  Tuple=16						,							A_					:  Optional[int]=2						,							A_					:  Optional[int]=0.02						,							A_					:  Any=4						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    seq_length
       lowerCamelCase_		     =    is_training
       lowerCamelCase_		     =    use_attention_mask
       lowerCamelCase_		     =    use_token_type_ids
       lowerCamelCase_		     =    use_labels
       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    intermediate_size
       lowerCamelCase_		     =    hidden_act
       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    max_position_embeddings
       lowerCamelCase_		     =    type_vocab_size
       lowerCamelCase_		     =    type_sequence_label_size
       lowerCamelCase_		     =    initializer_range
       lowerCamelCase_		     =    num_choices

 def 				a__ (				self					:  Dict  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )

       lowerCamelCase_		     =    None
       if self.use_attention_mask:
             lowerCamelCase_		     =    random_attention_mask([self.batch_size, self.seq_length]  )

       lowerCamelCase_		     =    None
       if self.use_token_type_ids:
             lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.type_vocab_size  )

       lowerCamelCase_		     =    AlbertConfig(
           vocab_size=self.vocab_size						,							hidden_size=self.hidden_size						,							num_hidden_layers=self.num_hidden_layers						,							num_attention_heads=self.num_attention_heads						,							intermediate_size=self.intermediate_size						,							hidden_act=self.hidden_act						,							hidden_dropout_prob=self.hidden_dropout_prob						,							attention_probs_dropout_prob=self.attention_probs_dropout_prob						,							max_position_embeddings=self.max_position_embeddings						,							type_vocab_size=self.type_vocab_size						,							is_decoder=A_						,							initializer_range=self.initializer_range						,							)

       return config, input_ids, token_type_ids, attention_mask





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

       """simple docstring"""






       lowerCamelCase_		     =    self.prepare_config_and_inputs()
       lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    config_and_inputs
       lowerCamelCase_		     =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
       return config, inputs_dict







@require_flax
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )

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

       """simple docstring"""






       lowerCamelCase_		     =    FlaxAlbertModelTester(self  )





 @slow
 def 				a__ (				self					:  Any  )   ->	int:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_		     =    model_class_name.from_pretrained('albert-base-v2'  )
             lowerCamelCase_		     =    model(np.ones((1, 1)  )  )
             self.assertIsNotNone(A_  )







@require_flax
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @slow
 def 				a__ (				self					:  str  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    FlaxAlbertModel.from_pretrained('albert-base-v2'  )
       lowerCamelCase_		     =    np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
       lowerCamelCase_		     =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_  )[0]
       lowerCamelCase_		     =    (1, 11, 768)
       self.assertEqual(output.shape						,							A_  )
       lowerCamelCase_		     =    np.array(
           [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]  )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,							A_						,							atol=1E-4  )  )









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   A:



 '''simple docstring'''


 UpperCamelCase						   =						BlenderbotConfig
 UpperCamelCase						   =						{}
 UpperCamelCase						   =						'''gelu'''

 def __init__(				self					:  int						,							A_					:  Optional[int]						,							A_					:  List[str]=13						,							A_					:  str=7						,							A_					:  Any=True						,							A_					:  Any=False						,							A_					:  Optional[Any]=99						,							A_					:  List[str]=32						,							A_					:  List[str]=2						,							A_					:  Dict=4						,							A_					:  List[str]=37						,							A_					:  List[str]=0.1						,							A_					:  Optional[int]=0.1						,							A_					:  str=20						,							A_					:  str=2						,							A_					:  Optional[Any]=1						,							A_					:  int=0						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    seq_length
       lowerCamelCase_		     =    is_training
       lowerCamelCase_		     =    use_labels
       lowerCamelCase_		     =    vocab_size
       lowerCamelCase_		     =    hidden_size
       lowerCamelCase_		     =    num_hidden_layers
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    intermediate_size

       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    max_position_embeddings
       lowerCamelCase_		     =    eos_token_id
       lowerCamelCase_		     =    pad_token_id
       lowerCamelCase_		     =    bos_token_id

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size  )
       lowerCamelCase_		     =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )						,							1  )
       lowerCamelCase_		     =    tf.concat([input_ids, eos_tensor]						,							axis=1  )

       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )

       lowerCamelCase_		     =    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_		     =    prepare_blenderbot_inputs_dict(A_						,							A_						,							A_  )
       return config, inputs_dict





 def 				a__ (				self					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  List[str]  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModel(config=A_  ).get_decoder()
       lowerCamelCase_		     =    inputs_dict['input_ids']

       lowerCamelCase_		     =    input_ids[:1, :]
       lowerCamelCase_		     =    inputs_dict['attention_mask'][:1, :]
       lowerCamelCase_		     =    inputs_dict['head_mask']
       lowerCamelCase_		     =    1

       # first forward pass
       lowerCamelCase_		     =    model(A_						,							attention_mask=A_						,							head_mask=A_						,							use_cache=A_  )

       lowerCamelCase_     ,				lowerCamelCase_		     =    outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowerCamelCase_		     =    ids_tensor((self.batch_size, 3)						,							config.vocab_size  )
       lowerCamelCase_		     =    tf.cast(ids_tensor((self.batch_size, 3)						,							2  )						,							tf.inta  )

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

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

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[str, Any]	,							lowercase	:       Any	,							lowercase	:       Tuple	,							lowercase	:       List[Any]=None	,							lowercase	:       List[str]=None	,							lowercase	:       List[Any]=None	,							lowercase	:       Tuple=None	,							lowercase	:       Union[str, Any]=None	,							):


      '''simple docstring'''



      if attention_mask is None:
            lowerCamelCase_		     =    tf.cast(tf.math.not_equal(lowercase	,							config.pad_token_id     )	,							tf.inta     )
      if decoder_attention_mask is None:
            lowerCamelCase_		     =    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_		     =    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            lowerCamelCase_		     =    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            lowerCamelCase_		     =    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(						UpperCamelCase							,      UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 UpperCamelCase						   =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 UpperCamelCase						   =						(
     {
         '''conversational''': TFBlenderbotForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotModel,
         '''summarization''': TFBlenderbotForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotForConditionalGeneration,
         '''translation''': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						   =						True
 UpperCamelCase						   =						False
 UpperCamelCase						   =						False

 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    TFBlenderbotModelTester(self  )
       lowerCamelCase_		     =    ConfigTester(self						,							config_class=A_  )

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

       """simple docstring"""






       self.config_tester.run_common_tests()





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

       """simple docstring"""






       lowerCamelCase_		     =    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						   =						['''My friends are cool but they eat too many carbs.''']
 UpperCamelCase						   =						'''facebook/blenderbot-400M-distill'''

 @cached_property
 def 				a__ (				self					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       return BlenderbotTokenizer.from_pretrained(self.model_name  )

 @cached_property
 def 				a__ (				self					:  List[Any]  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
       return model





 @slow
 def 				a__ (				self					:  str  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    self.tokenizer(self.src_text						,							return_tensors='tf'  )
       lowerCamelCase_		     =    self.model.generate(
           model_inputs.input_ids						,							)
       lowerCamelCase_		     =    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?"
       )



