





'''simple docstring'''







from typing import TYPE_CHECKING

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


A						    =							{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A						    =							[
        '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
        '''MODEL_FOR_BACKBONE_MAPPING''',
        '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
        '''MODEL_FOR_CAUSAL_LM_MAPPING''',
        '''MODEL_FOR_CTC_MAPPING''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
        '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
        '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
        '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
        '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
        '''MODEL_FOR_MASKED_LM_MAPPING''',
        '''MODEL_FOR_MASK_GENERATION_MAPPING''',
        '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
        '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
        '''MODEL_FOR_PRETRAINING_MAPPING''',
        '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
        '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
        '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
        '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
        '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
        '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
        '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
        '''MODEL_MAPPING''',
        '''MODEL_WITH_LM_HEAD_MAPPING''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
        '''AutoModel''',
        '''AutoBackbone''',
        '''AutoModelForAudioClassification''',
        '''AutoModelForAudioFrameClassification''',
        '''AutoModelForAudioXVector''',
        '''AutoModelForCausalLM''',
        '''AutoModelForCTC''',
        '''AutoModelForDepthEstimation''',
        '''AutoModelForImageClassification''',
        '''AutoModelForImageSegmentation''',
        '''AutoModelForInstanceSegmentation''',
        '''AutoModelForMaskGeneration''',
        '''AutoModelForTextEncoding''',
        '''AutoModelForMaskedImageModeling''',
        '''AutoModelForMaskedLM''',
        '''AutoModelForMultipleChoice''',
        '''AutoModelForNextSentencePrediction''',
        '''AutoModelForObjectDetection''',
        '''AutoModelForPreTraining''',
        '''AutoModelForQuestionAnswering''',
        '''AutoModelForSemanticSegmentation''',
        '''AutoModelForSeq2SeqLM''',
        '''AutoModelForSequenceClassification''',
        '''AutoModelForSpeechSeq2Seq''',
        '''AutoModelForTableQuestionAnswering''',
        '''AutoModelForTokenClassification''',
        '''AutoModelForUniversalSegmentation''',
        '''AutoModelForVideoClassification''',
        '''AutoModelForVision2Seq''',
        '''AutoModelForVisualQuestionAnswering''',
        '''AutoModelForDocumentQuestionAnswering''',
        '''AutoModelWithLMHead''',
        '''AutoModelForZeroShotImageClassification''',
        '''AutoModelForZeroShotObjectDetection''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A						    =							[
        '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
        '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
        '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
        '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
        '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
        '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
        '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
        '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
        '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
        '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
        '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
        '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
        '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
        '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
        '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
        '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
        '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
        '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
        '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
        '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
        '''TF_MODEL_MAPPING''',
        '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
        '''TFAutoModel''',
        '''TFAutoModelForAudioClassification''',
        '''TFAutoModelForCausalLM''',
        '''TFAutoModelForImageClassification''',
        '''TFAutoModelForMaskedImageModeling''',
        '''TFAutoModelForMaskedLM''',
        '''TFAutoModelForMaskGeneration''',
        '''TFAutoModelForMultipleChoice''',
        '''TFAutoModelForNextSentencePrediction''',
        '''TFAutoModelForPreTraining''',
        '''TFAutoModelForDocumentQuestionAnswering''',
        '''TFAutoModelForQuestionAnswering''',
        '''TFAutoModelForSemanticSegmentation''',
        '''TFAutoModelForSeq2SeqLM''',
        '''TFAutoModelForSequenceClassification''',
        '''TFAutoModelForSpeechSeq2Seq''',
        '''TFAutoModelForTableQuestionAnswering''',
        '''TFAutoModelForTextEncoding''',
        '''TFAutoModelForTokenClassification''',
        '''TFAutoModelForVision2Seq''',
        '''TFAutoModelForZeroShotImageClassification''',
        '''TFAutoModelWithLMHead''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A						    =							[
        '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
        '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
        '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
        '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
        '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
        '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
        '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
        '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
        '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
        '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
        '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
        '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
        '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
        '''FLAX_MODEL_MAPPING''',
        '''FlaxAutoModel''',
        '''FlaxAutoModelForCausalLM''',
        '''FlaxAutoModelForImageClassification''',
        '''FlaxAutoModelForMaskedLM''',
        '''FlaxAutoModelForMultipleChoice''',
        '''FlaxAutoModelForNextSentencePrediction''',
        '''FlaxAutoModelForPreTraining''',
        '''FlaxAutoModelForQuestionAnswering''',
        '''FlaxAutoModelForSeq2SeqLM''',
        '''FlaxAutoModelForSequenceClassification''',
        '''FlaxAutoModelForSpeechSeq2Seq''',
        '''FlaxAutoModelForTokenClassification''',
        '''FlaxAutoModelForVision2Seq''',
    ]


if TYPE_CHECKING:
    from .auto_factory import get_values
    from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
    from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
    from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
    from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
    from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_BACKBONE_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
            MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
            MODEL_FOR_MASK_GENERATION_MAPPING,
            MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            MODEL_FOR_MASKED_LM_MAPPING,
            MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            MODEL_FOR_OBJECT_DETECTION_MAPPING,
            MODEL_FOR_PRETRAINING_MAPPING,
            MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_TEXT_ENCODING_MAPPING,
            MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
            MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoBackbone,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForDepthEstimation,
            AutoModelForDocumentQuestionAnswering,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMaskGeneration,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeqaSeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeqaSeq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTextEncoding,
            AutoModelForTokenClassification,
            AutoModelForUniversalSegmentation,
            AutoModelForVideoClassification,
            AutoModelForVisionaSeq,
            AutoModelForVisualQuestionAnswering,
            AutoModelForZeroShotImageClassification,
            AutoModelForZeroShotObjectDetection,
            AutoModelWithLMHead,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_auto import (
            TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_MASK_GENERATION_MAPPING,
            TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            TF_MODEL_FOR_MASKED_LM_MAPPING,
            TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            TF_MODEL_FOR_PRETRAINING_MAPPING,
            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForAudioClassification,
            TFAutoModelForCausalLM,
            TFAutoModelForDocumentQuestionAnswering,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedImageModeling,
            TFAutoModelForMaskedLM,
            TFAutoModelForMaskGeneration,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSemanticSegmentation,
            TFAutoModelForSeqaSeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeqaSeq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTextEncoding,
            TFAutoModelForTokenClassification,
            TFAutoModelForVisionaSeq,
            TFAutoModelForZeroShotImageClassification,
            TFAutoModelWithLMHead,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_auto import (
            FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeqaSeqLM,
            FlaxAutoModelForSequenceClassification,
            FlaxAutoModelForSpeechSeqaSeq,
            FlaxAutoModelForTokenClassification,
            FlaxAutoModelForVisionaSeq,
        )

else:
    import sys

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






'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    A						    =							'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	int=None       ,  lowerCAmelCase__     :	List[Any]=None)			->						Union[str, Any]:

     '''simple docstring'''




     if attention_mask is None:
          _lowercase							:      str  						=							np.where(input_ids != config.pad_token_id       ,  1       ,  0)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }



@require_flax
class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : List[str]   ,UpperCamelCase    : Dict   ,UpperCamelCase    : Union[str, Any]=13   ,UpperCamelCase    : Optional[Any]=7   ,UpperCamelCase    : Optional[int]=True   ,UpperCamelCase    : Any=False   ,UpperCamelCase    : int=99   ,UpperCamelCase    : Optional[int]=16   ,UpperCamelCase    : Optional[int]=2   ,UpperCamelCase    : int=4   ,UpperCamelCase    : Optional[int]=4   ,UpperCamelCase    : Dict="gelu"   ,UpperCamelCase    : Optional[Any]=0.1   ,UpperCamelCase    : Optional[int]=0.1   ,UpperCamelCase    : str=20   ,UpperCamelCase    : List[Any]=2   ,UpperCamelCase    : Union[str, Any]=1   ,UpperCamelCase    : List[Any]=0   ,UpperCamelCase    : Optional[Any]=16   ,UpperCamelCase    : List[Any]=16   ,UpperCamelCase    : Optional[Any]=0.0_2   ,)		->  Dict:
           _lowercase							:      List[Any]  						=							parent
           _lowercase							:      Union[str, Any]  						=							batch_size
           _lowercase							:      int  						=							seq_length
           _lowercase							:      Dict  						=							is_training
           _lowercase							:      Tuple  						=							use_labels
           _lowercase							:      List[str]  						=							vocab_size
           _lowercase							:      str  						=							hidden_size
           _lowercase							:      List[str]  						=							num_hidden_layers
           _lowercase							:      List[str]  						=							num_attention_heads
           _lowercase							:      Dict  						=							intermediate_size
           _lowercase							:      Tuple  						=							hidden_act
           _lowercase							:      List[str]  						=							hidden_dropout_prob
           _lowercase							:      Optional[Any]  						=							attention_probs_dropout_prob
           _lowercase							:      List[str]  						=							max_position_embeddings
           _lowercase							:      Union[str, Any]  						=							eos_token_id
           _lowercase							:      Tuple  						=							pad_token_id
           _lowercase							:      str  						=							bos_token_id
           _lowercase							:      List[str]  						=							embed_dim
           _lowercase							:      Tuple  						=							word_embed_proj_dim
           _lowercase							:      Tuple  						=							initializer_range
           _lowercase							:      Any  						=							False

      def   _lowerCamelCase		(							self    : str  )		->  Union[str, Any]:
           _lowercase							:      str  						=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size  )   ,3   ,self.vocab_size  )
           _lowercase							:      int  						=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,dtype=np.intaa  ))   ,-1  )

           _lowercase							:      Optional[int]  						=							OPTConfig(
               vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,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_id=self.eos_token_id   ,bos_token_id=self.bos_token_id   ,pad_token_id=self.pad_token_id   ,embed_dim=self.embed_dim   ,is_encoder_decoder=UpperCamelCase   ,word_embed_proj_dim=self.word_embed_proj_dim   ,initializer_range=self.initializer_range   ,use_cache=UpperCamelCase   ,)
           _lowercase							:      int  						=							prepare_opt_inputs_dict(UpperCamelCase   ,UpperCamelCase  )
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Tuple  )		->  List[str]:
           _lowercase						, _lowercase							:      Tuple  						=							self.prepare_config_and_inputs()
           return config, inputs_dict

      def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : str   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : Optional[Any]  )		->  Optional[Any]:
           _lowercase							:      Dict  						=							20
           _lowercase							:      Any  						=							model_class_name(UpperCamelCase  )

           _lowercase							:      Dict  						=							inputs_dict['input_ids']
           _lowercase							:      Dict  						=							inputs_dict['attention_mask']

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      List[str]  						=							jnp.ones((input_ids.shape[0], max_length)   ,dtype='i4'  )

           _lowercase							:      Dict  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      str  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      Optional[int]  						=							model(
               input_ids[:, -1:]   ,attention_mask=UpperCamelCase   ,past_key_values=outputs_cache.past_key_values   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Dict  						=							model(UpperCamelCase  )

           _lowercase							:      Dict  						=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
           self.parent.assertTrue(diff < 1e-3   ,msg=F'''Max diff is {diff}'''  )



      def   _lowerCamelCase		(							self    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : str  )		->  Optional[Any]:
           _lowercase							:      Optional[int]  						=							20
           _lowercase							:      Dict  						=							model_class_name(UpperCamelCase  )

           _lowercase						, _lowercase							:      Tuple  						=							(
               inputs_dict['input_ids'],
               inputs_dict['attention_mask'],
           )

           _lowercase							:      int  						=							jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
               ]   ,axis=-1   ,)

           _lowercase							:      Dict  						=							model.init_cache(input_ids.shape[0]   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]   ,(input_ids.shape[0], input_ids.shape[-1] - 1)   ,)

           _lowercase							:      List[Any]  						=							model(
               input_ids[:, :-1]   ,attention_mask=UpperCamelCase   ,past_key_values=UpperCamelCase   ,position_ids=UpperCamelCase   ,)
           _lowercase							:      Any  						=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,dtype='i4'  )
           _lowercase							:      List[Any]  						=							model(
               input_ids[:, -1:]   ,past_key_values=outputs_cache.past_key_values   ,attention_mask=UpperCamelCase   ,position_ids=UpperCamelCase   ,)

           _lowercase							:      Tuple  						=							model(UpperCamelCase   ,attention_mask=UpperCamelCase  )

           _lowercase							:      List[Any]  						=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
           self.parent.assertTrue(diff < 1e-3   ,msg=F'''Max diff is {diff}'''  )



@require_flax
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   unittest.TestCase							,   lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      List[str] 		=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__       :      List[Any] 		=		() if is_flax_available() else ()

      def   _lowerCamelCase		(							self    : Optional[int]  )		->  str:
           _lowercase							:      Dict  						=							FlaxOPTModelTester(self  )

      def   _lowerCamelCase		(							self    : int  )		->  List[str]:
           _lowercase						, _lowercase							:      List[Any]  						=							self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Any  )		->  Dict:
           _lowercase						, _lowercase							:      Union[str, Any]  						=							self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )



      @slow
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  int:
           for model_class_name in self.all_model_classes:
                _lowercase							:      Optional[int]  						=							model_class_name.from_pretrained('facebook/opt-125m'  )
                _lowercase							:      Any  						=							np.ones((1, 1)  ) * model.config.eos_token_id
                _lowercase							:      str  						=							model(UpperCamelCase  )
                self.assertIsNotNone(UpperCamelCase  )



@require_sentencepiece
@require_flax
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @slow
      def   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Optional[int]  						=							FlaxOPTModel.from_pretrained('facebook/opt-350m'  )
           _lowercase							:      int  						=							jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
           _lowercase							:      Tuple  						=							model(input_ids=UpperCamelCase  ).last_hidden_state
           _lowercase							:      Union[str, Any]  						=							(1, 11, 512)
           self.assertEqual(output.shape   ,UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							jnp.array(
               [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]  )
           self.assertTrue(jnp.allclose(output[:, :3, :3]   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      def   _lowerCamelCase		(							self    : Optional[Any]  )		->  Tuple:
           super().setUp()
           _lowercase							:      Optional[int]  						=							'facebook/opt-350m'



      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Union[str, Any]:
           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(self.path_model  )
           _lowercase							:      str  						=							GPTaTokenizer.from_pretrained(self.path_model  )

           _lowercase							:      Any  						=							[
               'Today is a beautiful day and I want to',
               'In the city of',
               'Paris is the capital of France and',
               'Computers and mobile phones have taken',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           _lowercase							:      str  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase   ,add_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           _lowercase							:      int  						=							jnp.array(
               [
                   [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                   [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                   [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                   [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
               ]  )
           self.assertTrue(jnp.allclose(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )

           _lowercase							:      Any  						=							jax.jit(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							model(inputs.input_ids   ,attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           self.assertTrue(jnp.allclose(UpperCamelCase   ,UpperCamelCase   ,atol=4e-2  )  )



@require_flax
@slow
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      @property
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Optional[int]:
           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]

      def   _lowerCamelCase		(							self    : int  )		->  Optional[int]:
           _lowercase							:      Dict  						=							'facebook/opt-125m'

           _lowercase							:      Optional[int]  						=							[
               'Today is a beautiful day and I want to',
               'In the city of New York, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           _lowercase							:      int  						=							[]

           _lowercase							:      Tuple  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      List[str]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Optional[Any]  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Union[str, Any]  						=							generated_ids[0]

                _lowercase							:      Any  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]  )		->  Dict:
           _lowercase							:      int  						=							'facebook/opt-350m'

           _lowercase							:      Tuple  						=							[
               'Today is a beautiful day and I want to',
               'In the city of San Francisco, the city',
               'Paris is the capital of France and the capital',
               'Computers and mobile phones have taken over the',
           ]

           _lowercase							:      Union[str, Any]  						=							[]
           _lowercase							:      List[Any]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )

           for prompt in self.prompts:
                _lowercase							:      Optional[int]  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'  ).input_ids

                _lowercase							:      Tuple  						=							model.generate(UpperCamelCase   ,max_length=10  )
                _lowercase							:      Any  						=							generated_ids[0]

                _lowercase							:      Tuple  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )
                predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Tuple  )		->  Union[str, Any]:
           _lowercase							:      Union[str, Any]  						=							'facebook/opt-125m'
           _lowercase							:      List[Any]  						=							[
               'Today is a beautiful day and I want to thank',
               'In the city of Rome Canaver Canaver Canaver Canaver',
           ]
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer(
               [
                   'Today is a beautiful day and I want to',
                   'In the city of',
               ]   ,return_tensors='jax'   ,padding=UpperCamelCase   ,)

           _lowercase							:      str  						=							jax.jit(model.generate  )

           _lowercase							:      List[str]  						=							jit_generate(inputs['input_ids']   ,attention_mask=inputs['attention_mask']  ).sequences

           _lowercase							:      Union[str, Any]  						=							tokenizer.batch_decode(UpperCamelCase   ,skip_special_tokens=UpperCamelCase  )

           self.assertIsNotNone(UpperCamelCase   ,UpperCamelCase  )



      def   _lowerCamelCase		(							self    : Dict  )		->  List[str]:
           _lowercase							:      str  						=							'facebook/opt-350m'

           _lowercase							:      List[str]  						=							GPTaTokenizer.from_pretrained(UpperCamelCase  )
           _lowercase							:      List[str]  						=							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase  )

           _lowercase							:      int  						=							'left'

           # use different length sentences to test batching
           _lowercase							:      Tuple  						=							[
               'Hello, my dog is a little',
               'Today, I',
           ]

           _lowercase							:      int  						=							tokenizer(UpperCamelCase   ,return_tensors='jax'   ,padding=UpperCamelCase  )
           _lowercase							:      Union[str, Any]  						=							inputs['input_ids']

           _lowercase							:      str  						=							model.generate(input_ids=UpperCamelCase   ,attention_mask=inputs['attention_mask']   ,trace=UpperCamelCase  )

           _lowercase							:      Tuple  						=							tokenizer(sentences[0]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      Optional[Any]  						=							model.generate(input_ids=UpperCamelCase  )

           _lowercase							:      Optional[int]  						=							inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
           _lowercase							:      Optional[int]  						=							tokenizer(sentences[1]   ,return_tensors='jax'  ).input_ids
           _lowercase							:      List[str]  						=							model.generate(input_ids=UpperCamelCase   ,max_length=model.config.max_length - num_paddings  )

           _lowercase							:      Dict  						=							tokenizer.batch_decode(outputs[0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_non_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							tokenizer.decode(output_padded[0][0]   ,skip_special_tokens=UpperCamelCase  )

           _lowercase							:      Union[str, Any]  						=							[
               'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
               'Today, I was in the middle of a conversation with a friend about the',
           ]
           self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )
           self.assertListEqual(UpperCamelCase   ,[non_padded_sentence, padded_sentence]  )