



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   MBartConfig
    lowercase__      =   {}
    lowercase__      =   "gelu"

    def __init__(				self			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str]=1_3 ,  lowerCAmelCase_			:				Optional[int]=7 ,  lowerCAmelCase_			:				Optional[Any]=True ,  lowerCAmelCase_			:				Dict=False ,  lowerCAmelCase_			:				Union[str, Any]=9_9 ,  lowerCAmelCase_			:				Optional[int]=3_2 ,  lowerCAmelCase_			:				Optional[Any]=2 ,  lowerCAmelCase_			:				str=4 ,  lowerCAmelCase_			:				Dict=3_7 ,  lowerCAmelCase_			:				Dict=0.1 ,  lowerCAmelCase_			:				Any=0.1 ,  lowerCAmelCase_			:				List[Any]=2_0 ,  lowerCAmelCase_			:				Union[str, Any]=2 ,  lowerCAmelCase_			:				int=1 ,  lowerCAmelCase_			:				Optional[Any]=0 ,  ):







      """simple docstring"""







      lowercase_								= parent
      lowercase_								= batch_size
      lowercase_								= seq_length
      lowercase_								= is_training
      lowercase_								= use_labels
      lowercase_								= vocab_size
      lowercase_								= hidden_size
      lowercase_								= num_hidden_layers
      lowercase_								= num_attention_heads
      lowercase_								= intermediate_size
      lowercase_								= hidden_dropout_prob
      lowercase_								= attention_probs_dropout_prob
      lowercase_								= max_position_embeddings
      lowercase_								= eos_token_id
      lowercase_								= pad_token_id
      lowercase_								= bos_token_id

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







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

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

      lowercase_								= 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 ,  )
      lowercase_								= prepare_mbart_inputs_dict(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)
      return config, inputs_dict




    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= TFMBartModel(config=lowerCAmelCase_).get_decoder()
      lowercase_								= inputs_dict["""input_ids"""]

      lowercase_								= input_ids[:1, :]
      lowercase_								= inputs_dict["""attention_mask"""][:1, :]
      lowercase_								= inputs_dict["""head_mask"""]
      lowercase_								= 1

      # first forward pass
      lowercase_								= model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  head_mask=lowerCAmelCase_ ,  use_cache=lowerCAmelCase_)

      lowercase_					,    lowercase_								= outputs.to_tuple()
      lowercase_								= past_key_values[1]







def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  ) ->						List[Any]:







  '''simple docstring'''


  if attention_mask is None:
    lowercase_								= tf.cast(tf.math.not_equal(__lowerCAmelCase ,  config.pad_token_id							) ,  tf.inta							)
  if decoder_attention_mask is None:
    lowercase_								= 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:
    lowercase_								= tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
  if decoder_head_mask is None:
    lowercase_								= tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
  if cross_attn_head_mask is None:
    lowercase_								= 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    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase   ):
    lowercase__      =   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    lowercase__      =   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    lowercase__      =   (
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowercase__      =   True
    lowercase__      =   False
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

      return False

    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      lowercase_								= TFMBartModelTester(self)
      lowercase_								= ConfigTester(self ,  config_class=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      self.config_tester.run_common_tests()




    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_)







@require_sentencepiece
@require_tokenizers
@require_tf
class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):
    lowercase__      =   [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    lowercase__      =   [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    lowercase__      =   "facebook/mbart-large-en-ro"

    @cached_property
    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return AutoTokenizer.from_pretrained(self.model_name)

    @cached_property
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any ,  **lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_								= self.translate_src_text(**lowerCAmelCase_)
      self.assertListEqual(self.expected_text ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str] ,  **lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      lowercase_								= self.tokenizer(self.src_text ,  **lowerCAmelCase_ ,  return_tensors="""tf""")
      lowercase_								= self.model.generate(
          model_inputs.input_ids ,  attention_mask=model_inputs.attention_mask ,  num_beams=2)
      lowercase_								= self.tokenizer.batch_decode(lowerCAmelCase_ ,  skip_special_tokens=lowerCAmelCase_)
      return generated_words




    @slow
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      self._assert_generated_batch_equal_expected()






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

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


UpperCAmelCase						:		Any			= {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Any			= ["BlipImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Optional[int]			= [
           "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlipModel",
           "BlipPreTrainedModel",
           "BlipForConditionalGeneration",
           "BlipForQuestionAnswering",
           "BlipVisionModel",
           "BlipTextModel",
           "BlipForImageTextRetrieval",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFBlipModel",
           "TFBlipPreTrainedModel",
           "TFBlipForConditionalGeneration",
           "TFBlipForQuestionAnswering",
           "TFBlipVisionModel",
           "TFBlipTextModel",
           "TFBlipForImageTextRetrieval",
       ]

if TYPE_CHECKING:
       from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
       from .processing_blip import BlipProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_blip import BlipImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blip import (
                  BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlipForConditionalGeneration,
                  BlipForImageTextRetrieval,
                  BlipForQuestionAnswering,
                  BlipModel,
                  BlipPreTrainedModel,
                  BlipTextModel,
                  BlipVisionModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blip import (
                  TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFBlipForConditionalGeneration,
                  TFBlipForImageTextRetrieval,
                  TFBlipForQuestionAnswering,
                  TFBlipModel,
                  TFBlipPreTrainedModel,
                  TFBlipTextModel,
                  TFBlipVisionModel,
              )

else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

