



'''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 					snake_case			:
       lowerCAmelCase__	:List[Any]     =       MBartConfig
       lowerCAmelCase__	:Tuple     =       {}
       lowerCAmelCase__	:List[str]     =       "gelu"



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=13  ,UpperCAmelCase_=7  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,UpperCAmelCase_=99  ,UpperCAmelCase_=32  ,UpperCAmelCase_=2  ,UpperCAmelCase_=4  ,UpperCAmelCase_=37  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=20  ,UpperCAmelCase_=2  ,UpperCAmelCase_=1  ,UpperCAmelCase_=0  ,)	->				Union[str, Any]:
           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 				_a   (					self   )	->				Union[str, Any]:
           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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				List[str]:
           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 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    int  ,_snake_case	:    Tuple  ,_snake_case	:    int=None  ,_snake_case	:    Optional[int]=None  ,_snake_case	:    Dict=None  ,_snake_case	:    Tuple=None  ,_snake_case	:    Tuple=None  ,):





    '''simple docstring'''


    if attention_mask is None:
        lowercase__									=    tf.cast(tf.math.not_equal(_snake_case  ,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 					snake_case			(UpperCamelCase		,   UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[str]     =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
       lowerCAmelCase__	:Dict     =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
       lowerCAmelCase__	:Dict     =       (
           {
               "conversational": TFMBartForConditionalGeneration,
               "feature-extraction": TFMBartModel,
               "summarization": TFMBartForConditionalGeneration,
               "text2text-generation": TFMBartForConditionalGeneration,
               "translation": TFMBartForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase__	:Optional[Any]     =       True
       lowerCAmelCase__	:Optional[Any]     =       False
       lowerCAmelCase__	:int     =       False



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
               # Exception encountered when calling layer '...'
               return True

           return False



       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    TFMBartModelTester(self   )
           lowercase__									=    ConfigTester(self  ,config_class=UpperCAmelCase_   )



       def 				_a   (					self   )	->				str:
           self.config_tester.run_common_tests()







       def 				_a   (					self   )	->				List[Any]:
           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 					snake_case			(unittest.TestCase ):
       lowerCAmelCase__	:Union[str, Any]     =       [
           " UN Chief Says There Is No Military Solution in Syria",
       ]
       lowerCAmelCase__	:Optional[Any]     =       [
           "Şeful ONU declară că nu există o soluţie militară în Siria",
       ]
       lowerCAmelCase__	:Optional[int]     =       "facebook/mbart-large-en-ro"



       @cached_property
       def 				_a   (					self   )	->				int:
           return AutoTokenizer.from_pretrained(self.model_name   )



       @cached_property
       def 				_a   (					self   )	->				Dict:
           lowercase__									=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
           return model



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    self.translate_src_text(**UpperCAmelCase_   )
           self.assertListEqual(self.expected_text  ,UpperCAmelCase_   )



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				List[Any]:
           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 				_a   (					self   )	->				Union[str, Any]:
           self._assert_generated_batch_equal_expected()









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__          =  {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ClapModel",
     "ClapPreTrainedModel",
     "ClapTextModel",
     "ClapTextModelWithProjection",
     "ClapAudioModel",
     "ClapAudioModelWithProjection",
 ]
 SCREAMING_SNAKE_CASE__          =  ["ClapFeatureExtractor"]

if TYPE_CHECKING:
 from .configuration_clap import (
     CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
     ClapAudioConfig,
     ClapConfig,
     ClapTextConfig,
 )
 from .processing_clap import ClapProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clap import ClapFeatureExtractor
  from .modeling_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioModel,
      ClapAudioModelWithProjection,
      ClapModel,
      ClapPreTrainedModel,
      ClapTextModel,
      ClapTextModelWithProjection,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__          =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




