

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


A_	:			Dict  	=      logging.get_logger(__name__)

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			int )					->				Dict:
	try:
		with open(lowercase_   ,				"""rb""" ) as flax_state_f:
			A__							:   Optional[Any]					       =		from_bytes(lowercase_   ,				flax_state_f.read() )
	except UnpicklingError as e:
		try:
			with open(lowercase_ ) as f:
				if f.read().startswith("""version""" ):
					raise OSError(
					    """You seem to have cloned a repository without having git-lfs installed. Please"""
					    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
					    """ folder you cloned.""" )
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """ )

	return load_flax_weights_in_pytorch_model(lowercase_   ,				lowercase_ )


def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Dict )					->				Optional[int]:

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
		    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
		    """ instructions.""" )
		raise

	# check if we have bf16 weights
	A__							:   List[Any]					       =		flatten_dict(jax.tree_util.tree_map(lambda lowercase_ : x.dtype == jnp.bfloataa   ,				lowercase_ ) ).values()
	if any(lowercase_ ):
		# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

		# and bf16 is not fully supported in PT yet.
		logger.warning(
		    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
		    """before loading those in PyTorch model.""" )
		A__							:   Union[str, Any]					       =		jax.tree_util.tree_map(
		    lambda lowercase_ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params   ,				lowercase_ )

	A__							:   str					       =		""""""

	A__							:   Tuple					       =		flatten_dict(lowercase_   ,				sep=""".""" )
	A__							:   Dict					       =		pt_model.state_dict()

	# keep track of unexpected & missing keys
	A__							:   int					       =		[]
	A__							:   List[str]					       =		set(pt_model_dict.keys() )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		A__							:   Tuple					       =		flax_key_tuple.split(""".""" )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			A__							:   Optional[Any]					       =		flax_key_tuple_array[:-1] + ["""weight"""]
			A__							:   Tuple					       =		jnp.transpose(lowercase_   ,				(3, 2, 0, 1) )
		elif flax_key_tuple_array[-1] == "kernel":
			A__							:   int					       =		flax_key_tuple_array[:-1] + ["""weight"""]
			A__							:   str					       =		flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			A__							:   List[str]					       =		flax_key_tuple_array[:-1] + ["""weight"""]

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(lowercase_ ):
				A__							:   List[Any]					       =		(
				    flax_key_tuple_string.replace("""_0"""   ,				""".0""" )
				    .replace("""_1"""   ,				""".1""" )
				    .replace("""_2"""   ,				""".2""" )
				    .replace("""_3"""   ,				""".3""" )
				    .replace("""_4"""   ,				""".4""" )
				    .replace("""_5"""   ,				""".5""" )
				    .replace("""_6"""   ,				""".6""" )
				    .replace("""_7"""   ,				""".7""" )
				    .replace("""_8"""   ,				""".8""" )
				    .replace("""_9"""   ,				""".9""" )
				)

		A__							:   Optional[int]					       =		""".""".join(lowercase_ )

		if flax_key in pt_model_dict:
			if flax_tensor.shape != pt_model_dict[flax_key].shape:
				raise ValueError(
				    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
				    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
			else:
				# add weight to pytorch dict
				A__							:   Tuple					       =		np.asarray(lowercase_ ) if not isinstance(lowercase_   ,				np.ndarray ) else flax_tensor
				A__							:   Tuple					       =		torch.from_numpy(lowercase_ )
				# remove from missing keys
				missing_keys.remove(lowercase_ )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(lowercase_ )

	pt_model.load_state_dict(lowercase_ )

	# re-transform missing_keys to list
	A__							:   str					       =		list(lowercase_ )

	if len(lowercase_ ) > 0:
		logger.warning(
		    """Some weights of the Flax model were not used when initializing the PyTorch model"""
		    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
		    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
		    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
		    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
		    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
		    """ FlaxBertForSequenceClassification model).""" )
	if len(lowercase_ ) > 0:
		logger.warning(
		    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
		    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
		    """ use it for predictions and inference.""" )

	return pt_model






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_	:			Union[str, Any]  	=      logging.get_logger(__name__)


A_	:			int  	=      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_	:			Tuple  	=      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_	:			Tuple  	=      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_	:			Any  	=      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_	:			Union[str, Any]  	=      OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

A_	:			Union[str, Any]  	=      OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

A_	:			Tuple  	=      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_	:			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'),
    ]
)

A_	:			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'),
    ]
)

A_	:			Dict  	=      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_	:			List[str]  	=      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_	:			List[str]  	=      OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

A_	:			Optional[Any]  	=      OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

A_	:			Optional[Any]  	=      OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

A_	:			List[str]  	=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A_	:			int  	=      _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_	:			Any  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A_	:			Optional[Any]  	=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A_	:			Optional[int]  	=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A_	:			int  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A_	:			Optional[int]  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A_	:			Optional[int]  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A_	:			Tuple  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
A_	:			Union[str, Any]  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
A_	:			int  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
A_	:			int  	=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_MAPPING



A_	:			Any  	=      auto_class_update(FlaxAutoModel)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	FLAX_MODEL_FOR_PRETRAINING_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	FLAX_MODEL_FOR_MASKED_LM_MAPPING



A_	:			Tuple  	=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



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



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



A_	:			Tuple  	=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



A_	:			int  	=      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



A_	:			Dict  	=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



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



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



A_	:			Optional[Any]  	=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



A_	:			List[Any]  	=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



A_	:			List[str]  	=      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)



