






"""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        a     :
    UpperCAmelCase_ :     str	     =MBartConfig
    UpperCAmelCase_ :     int	     ={}
    UpperCAmelCase_ :     Optional[int]	     ="gelu"







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=1_3							,		_lowerCamelCase=7							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=9_9							,		_lowerCamelCase=3_2							,		_lowerCamelCase=2							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=2_0							,		_lowerCamelCase=2							,		_lowerCamelCase=1							,		_lowerCamelCase=0							,		):
      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       ):
      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(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__       )
      return config, inputs_dict







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       TFMBartModel(config=UpperCAmelCase__       ).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(UpperCAmelCase__							,		attention_mask=UpperCAmelCase__							,		head_mask=UpperCAmelCase__							,		use_cache=UpperCAmelCase__       )

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


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  Dict		,					__snake_case			:  Tuple		,					__snake_case			:  Union[str, Any]=None		,					__snake_case			:  Tuple=None		,					__snake_case			:  str=None		,					__snake_case			:  List[str]=None		,					__snake_case			:  Union[str, Any]=None		,					):





  '''simple docstring'''





  if attention_mask is None:
    lowercase							    =       tf.cast(tf.math.not_equal(lowercase_		,					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        a     (   UpperCAmelCase__,				UpperCAmelCase__,				unittest.TestCase	):
    UpperCAmelCase_ :     Tuple	     =(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    UpperCAmelCase_ :     int	     =(TFMBartForConditionalGeneration,) if is_tf_available() else ()
    UpperCAmelCase_ :     int	     =(
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCAmelCase_ :     str	     =True
    UpperCAmelCase_ :     Optional[int]	     =False
    UpperCAmelCase_ :     str	     =False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):

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

      return False







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       TFMBartModelTester(self       )
      lowercase							    =       ConfigTester(self							,		config_class=UpperCAmelCase__       )







    def 			UpperCamelCase_      (			self       ):
      self.config_tester.run_common_tests()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__       )


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







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      return AutoTokenizer.from_pretrained(self.model_name       )







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
      return model







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      lowercase							    =       self.translate_src_text(**UpperCAmelCase__       )
      self.assertListEqual(self.expected_text							,		UpperCAmelCase__       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      lowercase							    =       self.tokenizer(self.src_text							,		**UpperCAmelCase__							,		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(UpperCAmelCase__							,		skip_special_tokens=UpperCAmelCase__       )
      return generated_words







    @slow
    def 			UpperCamelCase_      (			self       ):
      self._assert_generated_batch_equal_expected()








from typing import TYPE_CHECKING

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


_lowerCamelCase	:		int							       =   {
    """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:
  _lowerCamelCase	:		Tuple							       =   ["""BlipImageProcessor"""]


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

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Optional[Any]							       =   [
      """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

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


