





"""simple docstring"""




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		:				Any  			=    logging.get_logger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					List[str]      )				->Optional[Any]:






	'''simple docstring'''






	try:
		with open(_lowercase					, "rb"      ) as flax_state_f:
			a   :	Union[str, 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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					Dict      )				->Tuple:






	'''simple docstring'''






	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   :	str			=       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   :	Optional[Any]			=       jax.tree_util.tree_map(
		    lambda _lowercase      : params.astype(np.floataa      ) if params.dtype == jnp.bfloataa else params					, _lowercase      )

	a   :	str			=       ""

	a   :	Union[str, Any]			=       flatten_dict(_lowercase					, sep="."      )
	a   :	Optional[int]			=       pt_model.state_dict()

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

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		a   :	List[Any]			=       flax_key_tuple.split("."      )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			a   :	Union[str, Any]			=       flax_key_tuple_array[:-1] + ["weight"]
			a   :	int			=       jnp.transpose(_lowercase					, (3, 2, 0, 1)      )
		elif flax_key_tuple_array[-1] == "kernel":
			a   :	List[Any]			=       flax_key_tuple_array[:-1] + ["weight"]
			a   :	Optional[int]			=       flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			a   :	Union[str, Any]			=       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   :	Dict			=       (
				    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   :	int			=       np.asarray(_lowercase      ) if not isinstance(_lowercase					, np.ndarray      ) else flax_tensor
				a   :	Optional[Any]			=       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   :	List[Any]			=       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









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


