

import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
       from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     if is_torch_version('<'     ,					'2.0.0'      ) or not hasattr(SCREAMING_SNAKE_CASE_     ,					'_dynamo'      ):
          return False
     return isinstance(SCREAMING_SNAKE_CASE_     ,					torch._dynamo.eval_frame.OptimizedModule      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   bool = True      ):



     '''simple docstring'''





     lowercase__   :       Dict				=	(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     lowercase__   :       List[str]				=	is_compiled_module(SCREAMING_SNAKE_CASE_      )
     if is_compiled:
          lowercase__   :       Optional[Any]				=	model
          lowercase__   :       int				=	model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Union[str, Any]				=	model.module

     if not keep_fpaa_wrapper:
          lowercase__   :       Dict				=	getattr(SCREAMING_SNAKE_CASE_     ,					'forward'      )
          lowercase__   :       str				=	model.__dict__.pop('_original_forward'     ,					SCREAMING_SNAKE_CASE_      )
          if original_forward is not None:
               while hasattr(SCREAMING_SNAKE_CASE_     ,					'__wrapped__'      ):
                    lowercase__   :       Any				=	forward.__wrapped__
                    if forward == original_forward:
                         break
               lowercase__   :       int				=	forward
          if getattr(SCREAMING_SNAKE_CASE_     ,					'_converted_to_transformer_engine'     ,					SCREAMING_SNAKE_CASE_      ):
               convert_model(SCREAMING_SNAKE_CASE_     ,					to_transformer_engine=SCREAMING_SNAKE_CASE_      )

     if is_compiled:
          lowercase__   :       Optional[int]				=	model
          lowercase__   :       Optional[Any]				=	compiled_model

     return model

def      snake_case__      (   ):



     '''simple docstring'''





     PartialState().wait_for_everyone()

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     elif PartialState().local_process_index == 0:
          torch.save(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

@contextmanager
def      snake_case__      (   **SCREAMING_SNAKE_CASE_		:   Tuple      ):



     '''simple docstring'''





     for key, value in kwargs.items():
          lowercase__   :       Tuple				=	str(SCREAMING_SNAKE_CASE_      )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



     '''simple docstring'''





     if not hasattr(SCREAMING_SNAKE_CASE_     ,					'__qualname__'      ) and not hasattr(SCREAMING_SNAKE_CASE_     ,					'__name__'      ):
          lowercase__   :       Optional[int]				=	getattr(SCREAMING_SNAKE_CASE_     ,					'__class__'     ,					SCREAMING_SNAKE_CASE_      )
     if hasattr(SCREAMING_SNAKE_CASE_     ,					'__qualname__'      ):
          return obj.__qualname__
     if hasattr(SCREAMING_SNAKE_CASE_     ,					'__name__'      ):
          return obj.__name__
     return str(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     for key, value in source.items():
          if isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       List[str]				=	destination.setdefault(SCREAMING_SNAKE_CASE_     ,					{}      )
               merge_dicts(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          else:
               lowercase__   :       Tuple				=	value

     return destination

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = None      ):



     '''simple docstring'''





     if port is None:
          lowercase__   :       int				=	29_500
     with socket.socket(socket.AF_INET     ,					socket.SOCK_STREAM      ) as s:
          return s.connect_ex(('localhost', port)      ) == 0




from collections import OrderedDict

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


snake_case_        =			logging.get_logger(__name__)


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'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

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

snake_case_        =			OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

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'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModel)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_PRETRAINING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[Any]														=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_MASKED_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[str]														=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)

