import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


_lowercase:	List[str]       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=False  ):
							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    'Loading a PyTorch model in Flax, 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

							if not is_sharded:
														_lowerCAmelCase							=							os.path.abspath(snake_case  )
														logger.info(F'Loading PyTorch weights from {pt_path}'  )

														_lowerCAmelCase							=							torch.load(snake_case     , map_location='cpu'  )
														logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters.'  )

														_lowerCAmelCase							=							convert_pytorch_state_dict_to_flax(snake_case     , snake_case  )
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														_lowerCAmelCase							=							convert_pytorch_sharded_state_dict_to_flax(snake_case     , snake_case  )
							return flax_state_dict






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case     , ):

							def is_key_or_prefix_key_in_dict(snake_case  ) -> bool:
														return len(set(snake_case  ) & {key, (model_prefix,) + key}  ) > 0

							# layer norm
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""scale""",)
							if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer mean
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""mean""",)
							if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case  ):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""var""",)
							if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case  ):
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""embedding""",)
							if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case  ):
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""kernel""",)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case  ):
														_lowerCAmelCase							=							pt_tensor.transpose(2     , 3     , 1     , 0  )
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""kernel""",)
							if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case  ):
														_lowerCAmelCase							=							pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""weight""",)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_lowerCAmelCase							=							pt_tuple_key[:-1] + ("""bias""",)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
							_lowerCAmelCase							=							None
							if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
														_lowerCAmelCase							=							pt_tuple_key[-2] + """_g"""
							elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
														_lowerCAmelCase							=							pt_tuple_key[-2] + """_v"""
							if name is not None:
														_lowerCAmelCase							=							pt_tuple_key[:-3] + (name,)
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor






def _lowerCamelCase  (		snake_case     , snake_case  ):
							# convert pytorch tensor to numpy
							_lowerCAmelCase							=							{k: v.numpy() for k, v in pt_state_dict.items()}

							_lowerCAmelCase							=							flax_model.base_model_prefix

							# use params dict if the model contains batch norm layers
							if "params" in flax_model.params:
														_lowerCAmelCase							=							flax_model.params["""params"""]
							else:
														_lowerCAmelCase							=							flax_model.params
							_lowerCAmelCase							=							flatten_dict(snake_case  )

							# add batch_stats keys,values to dict
							if "batch_stats" in flax_model.params:
														_lowerCAmelCase							=							flatten_dict(flax_model.params['batch_stats']  )
														random_flax_state_dict.update(snake_case  )

							_lowerCAmelCase							=							{}

							_lowerCAmelCase							=							(model_prefix not in flax_model_params) and (
							    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
							)
							_lowerCAmelCase							=							(model_prefix in flax_model_params) and (
							    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
							)

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_lowerCAmelCase							=							tuple(pt_key.split('.'  )  )

														# remove base model prefix if necessary
														_lowerCAmelCase							=							pt_tuple_key[0] == model_prefix
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_lowerCAmelCase							=							pt_tuple_key[1:]

														# Correctly rename weight parameters
														_lowerCAmelCase							=							rename_key_and_reshape_tensor(
														    snake_case     , snake_case     , snake_case     , snake_case  )

														# add model prefix if necessary
														_lowerCAmelCase							=							(model_prefix,) + flax_key in random_flax_state_dict
														if load_base_model_into_model_with_head and require_base_model_prefix:
																					_lowerCAmelCase							=							(model_prefix,) + flax_key

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
																												    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'  )

        # add batch stats if the model contains batchnorm layers
														if "batch_stats" in flax_model.params:
																					if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																												_lowerCAmelCase							=							jnp.asarray(snake_case  )
																												continue
																					# remove num_batches_tracked key
																					if "num_batches_tracked" in flax_key[-1]:
																												flax_state_dict.pop(snake_case     , snake_case  )
																												continue

																					# also add unexpected weight so that warning is thrown
																					_lowerCAmelCase							=							jnp.asarray(snake_case  )

														else:
																					# also add unexpected weight so that warning is thrown
																					_lowerCAmelCase							=							jnp.asarray(snake_case  )

							return unflatten_dict(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							import torch

							# Load the index
							_lowerCAmelCase							=							{}
							for shard_file in shard_filenames:
														# load using msgpack utils
														_lowerCAmelCase							=							torch.load(snake_case  )
														_lowerCAmelCase							=							{k: v.numpy() for k, v in pt_state_dict.items()}

														_lowerCAmelCase							=							flax_model.base_model_prefix

														# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
														if "batch_stats" in flax_model.params:
																					_lowerCAmelCase							=							flax_model.params["""params"""]

																					_lowerCAmelCase							=							flatten_dict(snake_case  )
																					random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']  )  )
														else:
																					_lowerCAmelCase							=							flax_model.params
																					_lowerCAmelCase							=							flatten_dict(snake_case  )

														_lowerCAmelCase							=							(model_prefix not in flax_model_params) and (
														    model_prefix in {k.split('.'  )[0] for k in pt_state_dict.keys()}
														)
														_lowerCAmelCase							=							(model_prefix in flax_model_params) and (
														    model_prefix not in {k.split('.'  )[0] for k in pt_state_dict.keys()}
														)
														# Need to change some parameters name to match Flax names
														for pt_key, pt_tensor in pt_state_dict.items():
																					_lowerCAmelCase							=							tuple(pt_key.split('.'  )  )

																					# remove base model prefix if necessary
																					_lowerCAmelCase							=							pt_tuple_key[0] == model_prefix
																					if load_model_with_head_into_base_model and has_base_model_prefix:
																												_lowerCAmelCase							=							pt_tuple_key[1:]

																					# Correctly rename weight parameters
																					_lowerCAmelCase							=							rename_key_and_reshape_tensor(
																					    snake_case     , snake_case     , snake_case     , snake_case  )
																					# add model prefix if necessary
																					_lowerCAmelCase							=							(model_prefix,) + flax_key in random_flax_state_dict
																					if load_base_model_into_model_with_head and require_base_model_prefix:
																												_lowerCAmelCase							=							(model_prefix,) + flax_key

																					if flax_key in random_flax_state_dict:
																												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																																			raise ValueError(
																																			    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
																																			    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'  )

            # add batch stats if the model contains batchnorm layers
																					if "batch_stats" in flax_model.params:
																												if "mean" in flax_key[-1]:
																																			_lowerCAmelCase							=							jnp.asarray(snake_case  )
																																			continue
																												if "var" in flax_key[-1]:
																																			_lowerCAmelCase							=							jnp.asarray(snake_case  )
																																			continue
																												# remove num_batches_tracked key
																												if "num_batches_tracked" in flax_key[-1]:
																																			flax_state_dict.pop(snake_case     , snake_case  )
																																			continue

																												# also add unexpected weight so that warning is thrown
																												_lowerCAmelCase							=							jnp.asarray(snake_case  )

																					else:
																												# also add unexpected weight so that warning is thrown
																												_lowerCAmelCase							=							jnp.asarray(snake_case  )
							return unflatten_dict(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							os.path.abspath(snake_case  )
							logger.info(F'Loading Flax weights from {flax_checkpoint_path}'  )

							# import correct flax class
							_lowerCAmelCase							=							getattr(snake_case     , 'Flax' + model.__class__.__name__  )

							# load flax weight dict
							with open(snake_case     , 'rb'  ) as state_f:
														try:
																					_lowerCAmelCase							=							from_bytes(snake_case     , state_f.read()  )
														except UnpicklingError:
																					raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '  )

							return load_flax_weights_in_pytorch_model(snake_case     , snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):

							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    'Loading a 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
							_lowerCAmelCase							=							flatten_dict(jax.tree_util.tree_map(lambda snake_case  : x.dtype == jnp.bfloataa     , snake_case  )  ).values()
							if any(snake_case  ):
														# convert all weights to fp32 if the 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.'  )
														_lowerCAmelCase							=							jax.tree_util.tree_map(
														    lambda snake_case  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params     , snake_case  )

							_lowerCAmelCase							=							flatten_dict(snake_case  )
							_lowerCAmelCase							=							pt_model.state_dict()

							_lowerCAmelCase							=							(pt_model.base_model_prefix in flax_state) and (
							    pt_model.base_model_prefix not in {k.split('.'  )[0] for k in pt_model_dict.keys()}
							)
							_lowerCAmelCase							=							(pt_model.base_model_prefix not in flax_state) and (
							    pt_model.base_model_prefix in {k.split('.'  )[0] for k in pt_model_dict.keys()}
							)

							# keep track of unexpected & missing keys
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							set(pt_model_dict.keys()  )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														_lowerCAmelCase							=							flax_key_tuple[0] == pt_model.base_model_prefix
														_lowerCAmelCase							=							""".""".join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

														# adapt flax_key to prepare for loading from/to base model only
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					_lowerCAmelCase							=							flax_key_tuple[1:]
														elif load_base_model_into_model_with_head and require_base_model_prefix:
																					_lowerCAmelCase							=							(pt_model.base_model_prefix,) + flax_key_tuple

														# rename flax weights to PyTorch format
														if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case  ) not in pt_model_dict:
																					# conv layer
																					_lowerCAmelCase							=							flax_key_tuple[:-1] + ("""weight""",)
																					_lowerCAmelCase							=							jnp.transpose(snake_case     , (3, 2, 0, 1)  )
														elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case  ) not in pt_model_dict:
																					# linear layer
																					_lowerCAmelCase							=							flax_key_tuple[:-1] + ("""weight""",)
																					_lowerCAmelCase							=							flax_tensor.T
														elif flax_key_tuple[-1] in ["scale", "embedding"]:
																					_lowerCAmelCase							=							flax_key_tuple[:-1] + ("""weight""",)

														# adding batch stats from flax batch norm to pt
														elif "mean" in flax_key_tuple[-1]:
																					_lowerCAmelCase							=							flax_key_tuple[:-1] + ("""running_mean""",)
														elif "var" in flax_key_tuple[-1]:
																					_lowerCAmelCase							=							flax_key_tuple[:-1] + ("""running_var""",)

														if "batch_stats" in flax_state:
																					_lowerCAmelCase							=							""".""".join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
														else:
																					_lowerCAmelCase							=							""".""".join(snake_case  )

														# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
														_lowerCAmelCase							=							{}
														# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
														for key in pt_model_dict:
																					_lowerCAmelCase							=							key.split('.'  )
																					_lowerCAmelCase							=							None
																					if key_components[-3::2] == ["parametrizations", "original0"]:
																												_lowerCAmelCase							=							key_components[-2] + """_g"""
																					elif key_components[-3::2] == ["parametrizations", "original1"]:
																												_lowerCAmelCase							=							key_components[-2] + """_v"""
																					if name is not None:
																												_lowerCAmelCase							=							key_components[:-3] + [name]
																												_lowerCAmelCase							=							""".""".join(snake_case  )
																												_lowerCAmelCase							=							key

														if flax_key in special_pt_names:
																					_lowerCAmelCase							=							special_pt_names[flax_key]

														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
																												_lowerCAmelCase							=							np.asarray(snake_case  ) if not isinstance(snake_case     , np.ndarray  ) else flax_tensor
																												_lowerCAmelCase							=							torch.from_numpy(snake_case  )
																												# remove from missing keys
																												missing_keys.remove(snake_case  )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(snake_case  )

							pt_model.load_state_dict(snake_case  )

							# re-transform missing_keys to list
							_lowerCAmelCase							=							list(snake_case  )

							if len(snake_case  ) > 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).'  )
							else:
														logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'  )
							if len(snake_case  ) > 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.'  )
							else:
														logger.warning(
														    F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
														    'If your task is similar to the task the model of the checkpoint was trained on, '
														    F'you can already use {pt_model.__class__.__name__} for predictions without further training.'  )

							return pt_model












import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


a__		     =     {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

a__		     =     logging.WARNING





def 				A__ ()     ->						Any:
					__UpperCamelCase					:	Union[str, Any]             =      os.getenv("""DATASETS_VERBOSITY"""		,	snake_case				)
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
															    F'''has to be one of: { ", ".join(log_levels.keys()				) }'''				)
					return _default_log_level





def 				A__ ()     ->						str:
					return __name__.split("""."""				)[0]





def 				A__ ()     ->						logging.Logger:
					return logging.getLogger(_get_library_name()				)





def 				A__ ()     ->						None:
					# Apply our default configuration to the library root logger.
					__UpperCamelCase					:	Any             =      _get_library_root_logger()
					library_root_logger.setLevel(_get_default_logging_level()				)





def 				A__ ()     ->						None:
					__UpperCamelCase					:	List[Any]             =      _get_library_root_logger()
					library_root_logger.setLevel(logging.NOTSET				)





def 				A__ (snake_case   :		Optional[str] = None				)     ->						logging.Logger:
					if name is None:
										__UpperCamelCase					:	Optional[int]             =      _get_library_name()
					return logging.getLogger(snake_case				)





def 				A__ ()     ->						int:
					return _get_library_root_logger().getEffectiveLevel()





def 				A__ (snake_case   :		int				)     ->						None:
					_get_library_root_logger().setLevel(snake_case				)





def 				A__ ()     ->						Dict:
					return set_verbosity(snake_case				)





def 				A__ ()     ->						Any:
					return set_verbosity(snake_case				)





def 				A__ ()     ->						Dict:
					return set_verbosity(snake_case				)





def 				A__ ()     ->						str:
					return set_verbosity(snake_case				)





def 				A__ ()     ->						None:
					__UpperCamelCase					:	str             =      False





def 				A__ ()     ->						None:
					__UpperCamelCase					:	List[str]             =      True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Optional[int]					,     *lowerCAmelCase		:						str					,     **lowerCAmelCase		:						List[str]	)	->       Union[str, Any]:  # pylint: disable=unused-argument







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      args[0] if args else None






	def __iter__(					self		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						return iter(self._iterator	)






	def __getattr__(					self		:						int					,     lowerCAmelCase		:						List[str]	)	->       Dict:







						"""simple docstring"""







						def empty_fn(*lowerCAmelCase		:						List[Any]					,     **lowerCAmelCase		:						Dict	):  # pylint: disable=unused-argument
											return

						return empty_fn






	def __enter__(					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						return self






	def __exit__(					self		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]	)	->       Tuple:







						"""simple docstring"""







						return






a__		     =     True


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __call__(					self		:						str					,     *lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Dict=False					,     **lowerCAmelCase		:						Tuple	)	->       Optional[Any]:







						"""simple docstring"""







						if _tqdm_active and not disable:
											return tqdm_lib.tqdm(*lowerCAmelCase					,     **lowerCAmelCase	)
						else:
											return EmptyTqdm(*lowerCAmelCase					,     **lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Any					,     *lowerCAmelCase		:						List[str]					,     **lowerCAmelCase		:						str	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      None
						if _tqdm_active:
											return tqdm_lib.tqdm.set_lock(*lowerCAmelCase					,     **lowerCAmelCase	)






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







						"""simple docstring"""







						if _tqdm_active:
											return tqdm_lib.tqdm.get_lock()






a__		     =     _tqdm_cls()





def 				A__ ()     ->						bool:
					global _tqdm_active
					return bool(_tqdm_active				)





def 				A__ ()     ->						List[str]:
					global _tqdm_active
					__UpperCamelCase					:	Tuple             =      True







def 				A__ ()     ->						int:
					global _tqdm_active
					__UpperCamelCase					:	Any             =      False






