

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__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			Tuple	)			->     List[Any]:

					"""simple docstring"""

					try:
										with open(__a		,'''rb'''	) as flax_state_f:
															_a	:    Union[str, Any]         =		from_bytes(__a		,flax_state_f.read()	)
					except UnpicklingError as e:
										try:
															with open(__a	) 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(__a		,__a	)







def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			Any	)			->     List[Any]:

					"""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	:    Any         =		flatten_dict(jax.tree_util.tree_map(lambda __a	: x.dtype == jnp.bfloataa		,__a	)	).values()
					if any(__a	):
										# 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	:    Tuple         =		jax.tree_util.tree_map(
										    lambda __a	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params		,__a	)

					_a	:    List[Any]         =		''''''

					_a	:    Any         =		flatten_dict(__a		,sep='''.'''	)
					_a	:    Optional[int]         =		pt_model.state_dict()

					# keep track of unexpected & missing keys
					_a	:    Any         =		[]
					_a	:    int         =		set(pt_model_dict.keys()	)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										_a	:    List[str]         =		flax_key_tuple.split('''.'''	)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															_a	:    int         =		flax_key_tuple_array[:-1] + ['''weight''']
															_a	:    Union[str, Any]         =		jnp.transpose(__a		,(3, 2, 0, 1)	)
										elif flax_key_tuple_array[-1] == "kernel":
															_a	:    List[Any]         =		flax_key_tuple_array[:-1] + ['''weight''']
															_a	:    int         =		flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															_a	:    Optional[int]         =		flax_key_tuple_array[:-1] + ['''weight''']

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(__a	):
																				_a	:    Union[str, 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	:    Tuple         =		'''.'''.join(__a	)

										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	:    str         =		np.asarray(__a	) if not isinstance(__a		,np.ndarray	) else flax_tensor
																				_a	:    List[str]         =		torch.from_numpy(__a	)
																				# remove from missing keys
																				missing_keys.remove(__a	)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__a	)

					pt_model.load_state_dict(__a	)

					# re-transform missing_keys to list
					_a	:    List[Any]         =		list(__a	)

					if len(__a	) > 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(__a	) > 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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				=		{'''configuration_mmbt''': ['''MMBTConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__				=		['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
			from .configuration_mmbt import MMBTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
			import sys

			a__				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



