
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 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							str     	=				BlenderbotConfig
   _SCREAMING_SNAKE_CASE				:							Dict     	=				{}
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				"gelu"
   def __init__(self			: str ,    snake_case_			: Optional[int] ,    snake_case_			: Dict=1_3 ,    snake_case_			: str=7 ,    snake_case_			: int=True ,    snake_case_			: int=False ,    snake_case_			: Optional[Any]=9_9 ,    snake_case_			: List[str]=3_2 ,    snake_case_			: Tuple=2 ,    snake_case_			: List[str]=4 ,    snake_case_			: int=3_7 ,    snake_case_			: int=0.1 ,    snake_case_			: Union[str, Any]=0.1 ,    snake_case_			: Union[str, Any]=2_0 ,    snake_case_			: int=2 ,    snake_case_			: int=1 ,    snake_case_			: List[str]=0 ,    ):
    __a			:  Optional[Any]		    =  parent
    __a			:  Any		    =  batch_size
    __a			:  Optional[Any]		    =  seq_length
    __a			:  Optional[int]		    =  is_training
    __a			:  Optional[Any]		    =  use_labels
    __a			:  List[Any]		    =  vocab_size
    __a			:  Tuple		    =  hidden_size
    __a			:  str		    =  num_hidden_layers
    __a			:  str		    =  num_attention_heads
    __a			:  Dict		    =  intermediate_size

    __a			:  Dict		    =  hidden_dropout_prob
    __a			:  Any		    =  attention_probs_dropout_prob
    __a			:  Any		    =  max_position_embeddings
    __a			:  str		    =  eos_token_id
    __a			:  List[str]		    =  pad_token_id
    __a			:  List[Any]		    =  bos_token_id
   def 				lowerCAmelCase      (self			: str       ):
    __a			:  Optional[Any]		    =  ids_tensor([self.batch_size, self.seq_length - 1] ,    self.vocab_size       )
    __a			:  Dict		    =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       ) ,    1       )
    __a			:  Tuple		    =  tf.concat([input_ids, eos_tensor] ,    axis=1       )

    __a			:  List[Any]		    =  ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size       )

    __a			:  Union[str, 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 ,    )
    __a			:  List[str]		    =  prepare_blenderbot_inputs_dict(_UpperCAmelCase ,    _UpperCAmelCase ,    _UpperCAmelCase       )
    return config, inputs_dict







   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Optional[int] ,    snake_case_			: Any       ):
    __a			:  List[Any]		    =  TFBlenderbotModel(config=_UpperCAmelCase       ).get_decoder()
    __a			:  Dict		    =  inputs_dict['''input_ids''']

    __a			:  List[Any]		    =  input_ids[:1, :]
    __a			:  int		    =  inputs_dict['''attention_mask'''][:1, :]
    __a			:  Union[str, Any]		    =  inputs_dict['''head_mask''']
    __a			:  List[Any]		    =  1

    # first forward pass
    __a			:  Tuple		    =  model(_UpperCAmelCase ,    attention_mask=_UpperCAmelCase ,    head_mask=_UpperCAmelCase ,    use_cache=_UpperCAmelCase       )

    __a			:  Dict		    =  outputs.to_tuple()

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

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

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

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





def        __UpperCamelCase(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[int]=None  ,       lowerCAmelCase__							:    int=None  ,       lowerCAmelCase__							:    List[str]=None  ,       lowerCAmelCase__							:    List[str]=None  ,       lowerCAmelCase__							:    int=None  ,       ):
 if attention_mask is None:
  __a			:  Union[str, Any]		    =  tf.cast(tf.math.not_equal(__UpperCAmelCase  ,       config.pad_token_id  )  ,       tf.inta  )
 if decoder_attention_mask is None:
  __a			:  Optional[int]		    =  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			:  int		    =  tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
 if decoder_head_mask is None:
  __a			:  Tuple		    =  tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
 if cross_attn_head_mask is None:
  __a			:  Optional[Any]		    =  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   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE				:							Dict     	=				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				(
       {
           "conversational": TFBlenderbotForConditionalGeneration,
           "feature-extraction": TFBlenderbotModel,
           "summarization": TFBlenderbotForConditionalGeneration,
           "text2text-generation": TFBlenderbotForConditionalGeneration,
           "translation": TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				True
   _SCREAMING_SNAKE_CASE				:							Dict     	=				False
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				False
   def 				lowerCAmelCase      (self			: str       ):
    __a			:  Optional[Any]		    =  TFBlenderbotModelTester(self       )
    __a			:  Any		    =  ConfigTester(self ,    config_class=_UpperCAmelCase       )
   def 				lowerCAmelCase      (self			: str       ):
    self.config_tester.run_common_tests()







   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Tuple		    =  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   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				["My friends are cool but they eat too many carbs."]
   _SCREAMING_SNAKE_CASE				:							str     	=				"facebook/blenderbot-400M-distill"
   @cached_property
   def 				lowerCAmelCase      (self			: Dict       ):
    return BlenderbotTokenizer.from_pretrained(self.model_name       )
   @cached_property
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  Union[str, Any]		    =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
    return model







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


from typing import TYPE_CHECKING

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


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
