






from collections import OrderedDict

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


__lowerCamelCase :      Any										=				logging.get_logger(__name__)


__lowerCamelCase :      List[Any]										=				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"""),
    ]
)

__lowerCamelCase :      List[Any]										=				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"""),
    ]
)

__lowerCamelCase :      Any										=				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"""),
    ]
)

__lowerCamelCase :      int										=				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"""),
    ]
)

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

__lowerCamelCase :      str										=				OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

__lowerCamelCase :      List[Any]										=				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"""),
    ]
)

__lowerCamelCase :      Optional[int]										=				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"""),
    ]
)

__lowerCamelCase :      Any										=				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"""),
    ]
)

__lowerCamelCase :      Any										=				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"""),
    ]
)

__lowerCamelCase :      Optional[Any]										=				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"""),
    ]
)

__lowerCamelCase :      int										=				OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

__lowerCamelCase :      List[Any]										=				OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

__lowerCamelCase :      Optional[int]										=				OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

__lowerCamelCase :      str										=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__lowerCamelCase :      str										=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__lowerCamelCase :      List[str]										=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__lowerCamelCase :      List[Any]										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__lowerCamelCase :      Optional[Any]										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase :      Dict										=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__lowerCamelCase :      Optional[Any]										=				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__lowerCamelCase :      Any										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase :      List[str]										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__lowerCamelCase :      Dict										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase :      Any										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__lowerCamelCase :      Dict										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__lowerCamelCase :      Dict										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__lowerCamelCase :      Optional[Any]										=				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_MAPPING
__lowerCamelCase :      Dict										=				auto_class_update(FlaxAutoModel)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_PRETRAINING_MAPPING
__lowerCamelCase :      Dict										=				auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__lowerCamelCase :      Tuple										=				auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_MASKED_LM_MAPPING
__lowerCamelCase :      Optional[Any]										=				auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__lowerCamelCase :      Optional[Any]										=				auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






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



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






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



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__lowerCamelCase :      List[Any]										=				auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__lowerCamelCase :      Any										=				auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__lowerCamelCase :      int										=				auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__lowerCamelCase :      Dict										=				auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__lowerCamelCase :      Optional[int]										=				auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")



class 							SCREAMING_SNAKE_CASE__						(    _BaseAutoModelClass						):





					"""simple docstring"""






					a_     =		FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__lowerCamelCase :      Union[str, Any]										=				auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



