

import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
     import torch


@is_pipeline_test
class  A						(							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    lowerCamelCase			=							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def 						snake_case__     (      self :       List[Any],lowercase_ :       Union[str, Any],lowercase_ :       int,lowercase_ :       Dict		)->    Any:





        '''simple docstring'''







        A__          =   TextaTextGenerationPipeline(model=lowercase_,tokenizer=lowercase_		)
        return generator, ["Something to write", "Something else"]


    def 						snake_case__     (      self :       int,lowercase_ :       List[str],lowercase_ :       List[Any]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   generator('Something there'		)
        self.assertEqual(lowercase_,[{'generated_text': ANY(lowercase_		)}]		)
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'		)		)

        A__          =   generator(['This is great !', 'Something else'],num_return_sequences=2,do_sample=lowercase_		)
        self.assertEqual(
            lowercase_,[
                [{'generated_text': ANY(lowercase_		)}, {'generated_text': ANY(lowercase_		)}],
                [{'generated_text': ANY(lowercase_		)}, {'generated_text': ANY(lowercase_		)}],
            ],)

        A__          =   generator(
            ['This is great !', 'Something else'],num_return_sequences=2,batch_size=2,do_sample=lowercase_		)
        self.assertEqual(
            lowercase_,[
                [{'generated_text': ANY(lowercase_		)}, {'generated_text': ANY(lowercase_		)}],
                [{'generated_text': ANY(lowercase_		)}, {'generated_text': ANY(lowercase_		)}],
            ],)

        with self.assertRaises(lowercase_		):
            generator(4		)


    @require_torch
    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        A__          =   pipeline('text2text-generation',model='patrickvonplaten/t5-tiny-random',framework='pt'		)
        # do_sample=False necessary for reproducibility
        A__          =   generator('Something there',do_sample=lowercase_		)
        self.assertEqual(lowercase_,[{'generated_text': ''}]		)

        A__          =   3
        A__          =   generator(
            'Something there',num_return_sequences=lowercase_,num_beams=lowercase_,)
        A__          =   [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(lowercase_,lowercase_		)

        A__          =   generator('This is a test',do_sample=lowercase_,num_return_sequences=2,return_tensors=lowercase_		)
        self.assertEqual(
            lowercase_,[
                {'generated_token_ids': ANY(torch.Tensor		)},
                {'generated_token_ids': ANY(torch.Tensor		)},
            ],)
        A__          =   generator.model.config.eos_token_id
        A__          =   '<pad>'
        A__          =   generator(
            ['This is a test', 'This is a second test'],do_sample=lowercase_,num_return_sequences=2,batch_size=2,return_tensors=lowercase_,)
        self.assertEqual(
            lowercase_,[
                [
                    {'generated_token_ids': ANY(torch.Tensor		)},
                    {'generated_token_ids': ANY(torch.Tensor		)},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor		)},
                    {'generated_token_ids': ANY(torch.Tensor		)},
                ],
            ],)


    @require_tf
    def 						snake_case__     (      self :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   pipeline('text2text-generation',model='patrickvonplaten/t5-tiny-random',framework='tf'		)
        # do_sample=False necessary for reproducibility
        A__          =   generator('Something there',do_sample=lowercase_		)
        self.assertEqual(lowercase_,[{'generated_text': ''}]		)








from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_						   =       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModel)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Any			=			FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	int			=			FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[int]			=			FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Tuple			=			FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")

class 		UpperCamelCase__ (      _BaseAutoModelClass   ):



     '''simple docstring'''

     __snake_case    :	Optional[Any]			=			FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_						   =       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)


