
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 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						BlenderbotSmallConfig
  a__       			=						{}
  a__       			=						'gelu'
  def __init__(					self     :					str     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Tuple=13     ,				UpperCamelCase__     :					Optional[int]=7     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					List[str]=False     ,				UpperCamelCase__     :					List[str]=99     ,				UpperCamelCase__     :					Tuple=32     ,				UpperCamelCase__     :					Any=2     ,				UpperCamelCase__     :					Any=4     ,				UpperCamelCase__     :					str=37     ,				UpperCamelCase__     :					List[Any]=0.1     ,				UpperCamelCase__     :					List[str]=0.1     ,				UpperCamelCase__     :					List[Any]=20     ,				UpperCamelCase__     :					Dict=2     ,				UpperCamelCase__     :					int=1     ,				UpperCamelCase__     :					Optional[Any]=0     ,				)     ->      Optional[Any]:







    """simple docstring"""






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

    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			eos_token_id
    __magic_name__								=			pad_token_id
    __magic_name__								=			bos_token_id
  def 				_lowercase				(					self     :					List[str]       )     ->      int:







    """simple docstring"""






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

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

    __magic_name__								=			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     ,				)
    __magic_name__								=			prepare_blenderbot_small_inputs_dict(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    return config, inputs_dict





  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			TFBlenderbotSmallModel(config=_UpperCAmelCase       ).get_decoder()
    __magic_name__								=			inputs_dict['input_ids']

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

    # first forward pass
    __magic_name__								=			model(_UpperCAmelCase     ,				attention_mask=_UpperCAmelCase     ,				head_mask=_UpperCAmelCase     ,				use_cache=_UpperCAmelCase       )

    __magic_name__								=			outputs.to_tuple()

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

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

    __magic_name__								=			model(_UpperCAmelCase     ,				attention_mask=_UpperCAmelCase       )[0]
    __magic_name__								=			model(_UpperCAmelCase     ,				attention_mask=_UpperCAmelCase     ,				past_key_values=_UpperCAmelCase       )[0]

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

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

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







def a__       (			A_,	A_,	A_,	A_=None,	A_=None,	A_=None,	A_=None,	A_=None,	):






  '''simple docstring'''





  if attention_mask is None:
    __magic_name__								=			tf.cast(tf.math.not_equal(lowerCAmelCase_,	config.pad_token_id   ),	tf.inta   )
  if decoder_attention_mask is None:
    __magic_name__								=			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:
    __magic_name__								=			tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
  if decoder_head_mask is None:
    __magic_name__								=			tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
  if cross_attn_head_mask is None:
    __magic_name__								=			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 			UpperCAmelCase_     (					_UpperCamelCase    ,			_UpperCamelCase    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						(
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  a__       			=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  a__       			=						(
      {
          'conversational': TFBlenderbotSmallForConditionalGeneration,
          'feature-extraction': TFBlenderbotSmallModel,
          'summarization': TFBlenderbotSmallForConditionalGeneration,
          'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
          'translation': TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  a__       			=						True
  a__       			=						False
  a__       			=						False
  def 				_lowercase				(					self     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__								=			TFBlenderbotSmallModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=_UpperCAmelCase       )
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    self.config_tester.run_common_tests()





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







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase       )







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


  '''simple docstring'''







  a__       			=						[
      '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?'
  ]
  a__       			=						'facebook/blenderbot_small-90M'
  @cached_property
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Tuple:







    """simple docstring"""






    return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""       )
  @cached_property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      str:







    """simple docstring"""






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





  @slow
  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






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

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Optional[int]      ,*_UpperCAmelCase   :			List[Any]      ,**_UpperCAmelCase   :			Union[str, Any]						):
									warnings.warn(
									    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use GLPNImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




