

from collections import OrderedDict

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


A__						=							logging.get_logger(__name__)


A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

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

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

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

A__						=							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"),
    ]
)

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

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

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

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

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Union[str, Any]       				=  FLAX_MODEL_MAPPING




A__						=							auto_class_update(FlaxAutoModel)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Union[str, Any]       				=  FLAX_MODEL_FOR_PRETRAINING_MAPPING




A__						=							auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Optional[Any]       				=  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




A__						=							auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Any       				=  FLAX_MODEL_FOR_MASKED_LM_MAPPING




A__						=							auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   int       				=  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Tuple       				=  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   str       				=  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




A__						=							auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   List[str]       				=  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   str       				=  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




A__						=							auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Optional[Any]       				=  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   str       				=  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Any       				=  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




A__						=							auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")

class 				__UpperCamelCase				(					_BaseAutoModelClass			):
	_lowercase  :   Optional[int]       				=  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




A__						=							auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def 					_lowercase     (				a_				:					str					,a_				:					str					,a_				:					str					,a_				:					Path					,a_				:					str = None					,a_				:					str = None					,a_				:					str = None					,)				->	Tuple:




					'''simple docstring'''







					if config_name_or_path is None:
										__magic_name__							=    'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

					if generator_tokenizer_name_or_path is None:
										__magic_name__							=    generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										__magic_name__							=    question_encoder_name_or_path

					__magic_name__							=    RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					__magic_name__							=    RagConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)
					__magic_name__							=    AutoConfig.from_pretrained(a_				)

					__magic_name__							=    gen_config
					__magic_name__							=    question_encoder_config

					__magic_name__							=    model_class.from_pretrained_question_encoder_generator(
					    a_					,a_					,config=a_				)
					rag_model.save_pretrained(a_				)

					# Sanity check.
					model_class.from_pretrained(a_				)

					# Save tokenizers.
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'				)
					__magic_name__							=    AutoTokenizer.from_pretrained(a_				)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'				)


if __name__ == "__main__":
						A__						=							argparse.ArgumentParser()
						parser.add_argument(
						    "--model_type",
						    choices=["rag_sequence", "rag_token"],
						    required=True,
						    type=str,
						    help="RAG model type: rag_sequence, rag_token",
						)
						parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
						parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
						parser.add_argument(
						    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
						)

						parser.add_argument(
						    "--generator_tokenizer_name_or_path",
						    type=str,
						    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
						)
						parser.add_argument(
						    "--question_encoder_tokenizer_name_or_path",
						    type=str,
						    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
						)
						parser.add_argument(
						    "--config_name_or_path",
						    type=str,
						    help=(
						        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
						        " ``model_type``"
						    ),
						)

						A__						=							parser.parse_args()

						A__						=							Path(args.dest)
						dest_dir.mkdir(exist_ok=True)

						consolidate(
						    args.model_type,
						    args.generator_name_or_path,
						    args.question_encoder_name_or_path,
						    dest_dir,
						    args.config_name_or_path,
						    args.generator_tokenizer_name_or_path,
						    args.question_encoder_tokenizer_name_or_path,
						)




