




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'''






    UpperCAmelCase__							  =				BlenderbotSmallConfig
    UpperCAmelCase__							  =				{}
    UpperCAmelCase__							  =				'''gelu'''


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





           '''simple docstring'''

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

           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   eos_token_id
           A__							=   pad_token_id
           A__							=   bos_token_id


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





           '''simple docstring'''

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

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

           A__							=   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 ,			)
           A__							=   prepare_blenderbot_small_inputs_dict(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)
           return config, inputs_dict





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   TFBlenderbotSmallModel(config=UpperCAmelCase__).get_decoder()
           A__							=   inputs_dict['''input_ids''']

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

           # first forward pass
           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			head_mask=UpperCAmelCase__ ,			use_cache=UpperCAmelCase__)

           A__			,							A__							=   outputs.to_tuple()

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

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

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)[0]
           A__							=   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
           A__							=   int(ids_tensor((1,) ,			output_from_past.shape[-1]))
           A__							=   output_from_no_past[:, -3:, random_slice_idx]
           A__							=   output_from_past[:, :, random_slice_idx]

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    )       ->	Dict:



       """simple docstring"""

       if attention_mask is None:
              A__							=   tf.cast(tf.math.not_equal(lowercase_			,    config.pad_token_id						)			,    tf.inta						)
       if decoder_attention_mask is None:
              A__							=   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:
              A__							=   tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
       if decoder_head_mask is None:
              A__							=   tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
       if cross_attn_head_mask is None:
              A__							=   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'''






    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 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[Any]:





           '''simple docstring'''

           A__							=   TFBlenderbotSmallModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__)


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





           '''simple docstring'''

           self.config_tester.run_common_tests()





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





           '''simple docstring'''

           A__							=   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'''






    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 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->str:





           '''simple docstring'''

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


    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->str:





           '''simple docstring'''

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





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[Any]:





           '''simple docstring'''

           A__							=   self.tokenizer(self.src_text ,			return_tensors='''tf''')
           A__							=   self.model.generate(
               model_inputs.input_ids ,			attention_mask=model_inputs.attention_mask ,			num_beams=2 ,			use_cache=UpperCAmelCase__ ,			)
           A__							=   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.",
           )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowerCamelCase     :		Optional[Any]								=       {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Any								=       [
      """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TrOCRForCausalLM""",
      """TrOCRPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


