


'''simple docstring'''



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 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Optional[int]				=       FLAX_MODEL_MAPPING

__A						=auto_class_update(FlaxAutoModel)


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Any				=       FLAX_MODEL_FOR_PRETRAINING_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Optional[int]				=       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :str				=       FLAX_MODEL_FOR_MASKED_LM_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :List[Any]				=       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 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :List[str]				=       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Dict				=       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :List[Any]				=       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :List[Any]				=       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Dict				=       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Optional[Any]				=       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :Union[str, Any]				=       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

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


class 		_snake_case       (  _BaseAutoModelClass						):
					lowerCAmelCase   :List[str]				=       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

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





import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


a__		     =     get_tests_dir('''fixtures/dummy-config.json''')


class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						str	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      0






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[str]:







						"""simple docstring"""







						self.assertIsNotNone(transformers.models.auto.__spec__	)
						self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""	)	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      AutoConfig.from_pretrained("""bert-base-uncased"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      AutoConfig.from_pretrained(lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      AutoConfig.from_pretrained(lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      AutoConfig.for_model("""roberta"""	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						with tempfile.TemporaryDirectory() as tmp_dir:
											# This model name contains bert and roberta, but roberta ends up being picked.
											__UpperCamelCase					:	Union[str, Any]             =      os.path.join(lowerCAmelCase					,     """fake-roberta"""	)
											os.makedirs(lowerCAmelCase					,     exist_ok=lowerCAmelCase	)
											with open(os.path.join(lowerCAmelCase					,     """config.json"""	)					,     """w"""	) as f:
																f.write(json.dumps({}	)	)
											__UpperCamelCase					:	Dict             =      AutoConfig.from_pretrained(lowerCAmelCase	)
											self.assertEqual(type(lowerCAmelCase	)					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int	)	->       str:







						"""simple docstring"""







						try:
											AutoConfig.register("""custom"""					,     lowerCAmelCase	)
											# Wrong model type will raise an error
											with self.assertRaises(lowerCAmelCase	):
																AutoConfig.register("""model"""					,     lowerCAmelCase	)
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(lowerCAmelCase	):
																AutoConfig.register("""bert"""					,     lowerCAmelCase	)

											# Now that the config is registered, it can be used as any other config with the auto-API
											__UpperCamelCase					:	Optional[Any]             =      CustomConfig()
											with tempfile.TemporaryDirectory() as tmp_dir:
																config.save_pretrained(lowerCAmelCase	)
																__UpperCamelCase					:	List[str]             =      AutoConfig.from_pretrained(lowerCAmelCase	)
																self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)

						finally:
											if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]






	def 					lowerCamelCase__     (					self		:						int	)	->       Optional[Any]:







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     """bert-base is not a local folder and is not a valid model identifier"""	):
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""bert-base"""	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Any:







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""	):
											__UpperCamelCase					:	Optional[Any]             =      AutoConfig.from_pretrained(lowerCAmelCase					,     revision="""aaaaaa"""	)






	def 					lowerCamelCase__     (					self		:						str	)	->       List[str]:







						"""simple docstring"""







						with self.assertRaisesRegex(
						    lowerCAmelCase					,     """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""					,     ):
											__UpperCamelCase					:	Union[str, Any]             =      AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Optional[int]:







						"""simple docstring"""







						with self.assertRaises(lowerCAmelCase	):
											__UpperCamelCase					:	str             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	)
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(lowerCAmelCase	):
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
						self.assertEqual(config.__class__.__name__					,     """NewModelConfig"""	)

						# Test config can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(lowerCAmelCase	)
											__UpperCamelCase					:	str             =      AutoConfig.from_pretrained(lowerCAmelCase					,     trust_remote_code=lowerCAmelCase	)
						self.assertEqual(reloaded_config.__class__.__name__					,     """NewModelConfig"""	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       str:







						"""simple docstring"""







						class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



							"""simple docstring"""


							__magic_name__   :       int      	=		'new-model'

						try:
											AutoConfig.register("""new-model"""					,     lowerCAmelCase	)
											# If remote code is not set, the default is to use local
											__UpperCamelCase					:	Tuple             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfigLocal"""	)

											# If remote code is disabled, we load the local one.
											__UpperCamelCase					:	Any             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfigLocal"""	)

											# If remote is enabled, we load from the Hub
											__UpperCamelCase					:	List[Any]             =      AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,     trust_remote_code=lowerCAmelCase	)
											self.assertEqual(config.__class__.__name__					,     """NewModelConfig"""	)

						finally:
											if "new-model" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["new-model"]






