
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     :	Dict    = logging.get_logger(__name__)


A     :	List[str]    = 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     :	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'),
    ]
)

A     :	Union[str, 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'),
    ]
)

A     :	Optional[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'),
    ]
)

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

A     :	Any    = OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

A     :	Optional[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'),
    ]
)

A     :	List[Any]    = 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     :	Optional[int]    = 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     :	Optional[int]    = 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     :	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'),
    ]
)

A     :	Union[str, Any]    = OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

A     :	Tuple    = OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

A     :	List[Any]    = OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

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







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_MAPPING



A     :	Union[str, Any]    = auto_class_update(FlaxAutoModel)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_PRETRAINING_MAPPING



A     :	List[str]    = auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



A     :	Dict    = auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_MASKED_LM_MAPPING



A     :	List[str]    = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



A     :	List[Any]    = auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



A     :	Optional[int]    = auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



A     :	Union[str, Any]    = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



A     :	int    = auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



A     :	Union[str, Any]    = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



A     :	Union[str, Any]    = auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



A     :	Tuple    = auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



A     :	str    = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')







class 			__A( _BaseAutoModelClass			):
				snake_case_						   =    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



A     :	str    = auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)

# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


A     :	Optional[Any]    = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

A     :	List[str]    = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A     :	Optional[int]    = 'zero2'
A     :	str    = 'zero3'
A     :	Tuple    = [ZEROa, ZEROa]


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Tuple:
				# customize the test name generator function as we want both params to appear in the sub-test
				# name, as by default it shows only the first param
				__a										=    parameterized.to_safe_name('''_'''.join(str(a__  ) for x in param.args  )  )
				return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
A     :	Union[str, Any]    = list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 			__A( a			):




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      str:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    models[model]

								__a										=    self.run_trainer(
								    stage=_snake_case		,     model_name=_snake_case		,     eval_steps=_snake_case		,     num_train_epochs=1		,     distributed=_snake_case		,     fpaa=_snake_case		,     )

								self.do_checks(_snake_case				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = 1		,     _snake_case = True		,     _snake_case = True		,     )    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_auto_remove_tmp_dir('''./xxx'''		,     after=_snake_case				)
								__a										=    F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_snake_case				)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

								if fpaa:
												args.extend(['''--fp16''']				)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__a										=    F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
								__a										=    [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
								__a										=    self.get_launcher(_snake_case				)

								__a										=    launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(_snake_case		,     env=self.get_env()				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      List[str]:


								'''simple docstring'''




								__a										=    min(2		,     get_gpu_count()				) if distributed else 1
								return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()