





"""simple docstring"""
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							:
 snake_case__             =       BlenderbotSmallConfig
 snake_case__             =       {}
 snake_case__             =       '''gelu'''



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







  """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    UpperCamelCase__  (     self     ):







  """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_small_inputs_dict(_snake_case				,       _snake_case				,       _snake_case     )
  return config, inputs_dict



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFBlenderbotSmallModel(config=_snake_case     ).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(_snake_case				,       attention_mask=_snake_case				,       head_mask=_snake_case				,       use_cache=_snake_case     )

  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(_snake_case				,       attention_mask=_snake_case     )[0]
  lowerCAmelCase    				= model(_snake_case				,       attention_mask=_snake_case				,       past_key_values=_snake_case     )[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(_snake_case				,       _snake_case				,       rtol=1E-3     )


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Optional[int]=None  ,     _UpperCAmelCase					:    Dict=None  ,     _UpperCAmelCase					:    Optional[int]=None  ,     _UpperCAmelCase					:    Union[str, Any]=None  ,     _UpperCAmelCase					:    str=None  ,     ):
 if attention_mask is None:
  lowerCAmelCase    				= tf.cast(tf.math.not_equal(_UpperCAmelCase  ,     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							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 snake_case__             =       (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 snake_case__             =       (
     {
         '''conversational''': TFBlenderbotSmallForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotSmallModel,
         '''summarization''': TFBlenderbotSmallForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
         '''translation''': TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 snake_case__             =       True
 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFBlenderbotSmallModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*_snake_case     )







@require_tokenizers
@require_tf
class       a							(   unittest.TestCase  ):
 snake_case__             =       [
     '''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?'''
 ]
 snake_case__             =       '''facebook/blenderbot_small-90M'''



 @cached_property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 @cached_property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.tokenizer(self.src_text				,       return_tensors='tf'     )
  lowerCAmelCase    				= self.model.generate(
      model_inputs.input_ids				,       attention_mask=model_inputs.attention_mask				,       num_beams=2				,       use_cache=_snake_case				,       )
  lowerCAmelCase    				= self.tokenizer.batch_decode(generated_ids.numpy()				,       skip_special_tokens=_snake_case     )[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.",
  )












"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





