

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


A_			:					str    			=     logging.get_logger(__name__)

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					int       , lowercase_:					List[str]       , lowercase_:					Optional[int]=False   )	-> Dict:
	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:
		A__				:       Union[str, Any]	  =						os.path.abspath(lowercase_   )
		logger.info(f"""Loading PyTorch weights from {pt_path}"""   )

		A__				:       Union[str, Any]	  =						torch.load(lowercase_       , map_location="""cpu"""   )
		logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."""   )

		A__				:       List[Any]	  =						convert_pytorch_state_dict_to_flax(lowercase_       , lowercase_   )
	else:
		# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
		A__				:       Union[str, Any]	  =						convert_pytorch_sharded_state_dict_to_flax(lowercase_       , lowercase_   )
	return flax_state_dict

def 		UpperCamelCase   (lowercase_:					Tuple[str]       , lowercase_:					np.ndarray       , lowercase_:					Dict[str, jnp.ndarray]       , lowercase_:					str       , )	-> (Tuple[str], np.ndarray):

	def is_key_or_prefix_key_in_dict(lowercase_:					Tuple[str]   ) -> bool:
		return len(set(lowercase_   ) & {key, (model_prefix,) + key}   ) > 0

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

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

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

	# embedding
	A__				:       List[str]	  =						pt_tuple_key[:-1] + ("""embedding""",)
	if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowercase_   ):
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	A__				:       Union[str, Any]	  =						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(lowercase_   ):
		A__				:       Optional[Any]	  =						pt_tensor.transpose(2       , 3       , 1       , 0   )
		return renamed_pt_tuple_key, pt_tensor

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

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

	# old PyTorch layer norm bias
	A__				:       Optional[Any]	  =						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
	A__				:       List[str]	  =						None
	if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
		A__				:       Optional[Any]	  =						pt_tuple_key[-2] + """_g"""
	elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
		A__				:       str	  =						pt_tuple_key[-2] + """_v"""
	if name is not None:
		A__				:       Union[str, Any]	  =						pt_tuple_key[:-3] + (name,)
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					List[Any]   )	-> Optional[int]:
	# convert pytorch tensor to numpy
	A__				:       Union[str, Any]	  =						{k: v.numpy() for k, v in pt_state_dict.items()}

	A__				:       str	  =						flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		A__				:       Any	  =						flax_model.params["""params"""]
	else:
		A__				:       Optional[Any]	  =						flax_model.params
	A__				:       Tuple	  =						flatten_dict(lowercase_   )

	# add batch_stats keys,values to dict
	if "batch_stats" in flax_model.params:
		A__				:       List[str]	  =						flatten_dict(flax_model.params["""batch_stats"""]   )
		random_flax_state_dict.update(lowercase_   )

	A__				:       List[str]	  =						{}

	A__				:       Optional[Any]	  =						(model_prefix not in flax_model_params) and (
	    model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
	)
	A__				:       Union[str, Any]	  =						(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():
		A__				:       Optional[Any]	  =						tuple(pt_key.split("""."""   )   )

		# remove base model prefix if necessary
		A__				:       int	  =						pt_tuple_key[0] == model_prefix
		if load_model_with_head_into_base_model and has_base_model_prefix:
			A__				:       List[str]	  =						pt_tuple_key[1:]

		# Correctly rename weight parameters
		A__  ,  A__				:       str	  =						rename_key_and_reshape_tensor(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_   )

		# add model prefix if necessary
		A__				:       List[str]	  =						(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			A__				:       Dict	  =						(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]:
				A__				:       Tuple	  =						jnp.asarray(lowercase_   )
				continue
			# remove num_batches_tracked key
			if "num_batches_tracked" in flax_key[-1]:
				flax_state_dict.pop(lowercase_       , lowercase_   )
				continue

			# also add unexpected weight so that warning is thrown
			A__				:       Tuple	  =						jnp.asarray(lowercase_   )

		else:
			# also add unexpected weight so that warning is thrown
			A__				:       Union[str, Any]	  =						jnp.asarray(lowercase_   )

	return unflatten_dict(lowercase_   )

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Union[str, Any]   )	-> Tuple:
	import torch

	# Load the index
	A__				:       Tuple	  =						{}
	for shard_file in shard_filenames:
		# load using msgpack utils
		A__				:       Optional[int]	  =						torch.load(lowercase_   )
		A__				:       int	  =						{k: v.numpy() for k, v in pt_state_dict.items()}

		A__				:       Optional[Any]	  =						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:
			A__				:       List[str]	  =						flax_model.params["""params"""]

			A__				:       Optional[int]	  =						flatten_dict(lowercase_   )
			random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
		else:
			A__				:       Dict	  =						flax_model.params
			A__				:       Tuple	  =						flatten_dict(lowercase_   )

		A__				:       int	  =						(model_prefix not in flax_model_params) and (
		    model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
		)
		A__				:       Dict	  =						(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():
			A__				:       Union[str, Any]	  =						tuple(pt_key.split("""."""   )   )

			# remove base model prefix if necessary
			A__				:       Optional[int]	  =						pt_tuple_key[0] == model_prefix
			if load_model_with_head_into_base_model and has_base_model_prefix:
				A__				:       Union[str, Any]	  =						pt_tuple_key[1:]

			# Correctly rename weight parameters
			A__  ,  A__				:       Any	  =						rename_key_and_reshape_tensor(
			    lowercase_       , lowercase_       , lowercase_       , lowercase_   )
			# add model prefix if necessary
			A__				:       Union[str, Any]	  =						(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				A__				:       List[Any]	  =						(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]:
					A__				:       Optional[Any]	  =						jnp.asarray(lowercase_   )
					continue
				if "var" in flax_key[-1]:
					A__				:       Any	  =						jnp.asarray(lowercase_   )
					continue
				# remove num_batches_tracked key
				if "num_batches_tracked" in flax_key[-1]:
					flax_state_dict.pop(lowercase_       , lowercase_   )
					continue

				# also add unexpected weight so that warning is thrown
				A__				:       Optional[int]	  =						jnp.asarray(lowercase_   )

			else:
				# also add unexpected weight so that warning is thrown
				A__				:       Union[str, Any]	  =						jnp.asarray(lowercase_   )
	return unflatten_dict(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					Dict   )	-> Optional[Any]:
	A__				:       str	  =						os.path.abspath(lowercase_   )
	logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""   )

	# import correct flax class
	A__				:       Any	  =						getattr(lowercase_       , """Flax""" + model.__class__.__name__   )

	# load flax weight dict
	with open(lowercase_       , """rb"""   ) as state_f:
		try:
			A__				:       int	  =						from_bytes(lowercase_       , 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(lowercase_       , lowercase_   )


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

	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
	A__				:       Tuple	  =						flatten_dict(jax.tree_util.tree_map(lambda lowercase_   : x.dtype == jnp.bfloataa       , lowercase_   )   ).values()
	if any(lowercase_   ):
		# 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."""   )
		A__				:       List[str]	  =						jax.tree_util.tree_map(
		    lambda lowercase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params       , lowercase_   )

	A__				:       Any	  =						flatten_dict(lowercase_   )
	A__				:       Any	  =						pt_model.state_dict()

	A__				:       Any	  =						(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()}
	)
	A__				:       Any	  =						(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
	A__				:       Tuple	  =						[]
	A__				:       List[str]	  =						set(pt_model_dict.keys()   )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		A__				:       Optional[Any]	  =						flax_key_tuple[0] == pt_model.base_model_prefix
		A__				:       int	  =						""".""".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:
			A__				:       Optional[int]	  =						flax_key_tuple[1:]
		elif load_base_model_into_model_with_head and require_base_model_prefix:
			A__				:       List[str]	  =						(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(lowercase_   ) not in pt_model_dict:
			# conv layer
			A__				:       Tuple	  =						flax_key_tuple[:-1] + ("""weight""",)
			A__				:       str	  =						jnp.transpose(lowercase_       , (3, 2, 0, 1)   )
		elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase_   ) not in pt_model_dict:
			# linear layer
			A__				:       Any	  =						flax_key_tuple[:-1] + ("""weight""",)
			A__				:       Dict	  =						flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
			A__				:       Tuple	  =						flax_key_tuple[:-1] + ("""weight""",)

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

		if "batch_stats" in flax_state:
			A__				:       List[Any]	  =						""".""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
		else:
			A__				:       Optional[Any]	  =						""".""".join(lowercase_   )

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

		if flax_key in special_pt_names:
			A__				:       Union[str, Any]	  =						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
				A__				:       Optional[Any]	  =						np.asarray(lowercase_   ) if not isinstance(lowercase_       , np.ndarray   ) else flax_tensor
				A__				:       Dict	  =						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__				:       Optional[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)."""   )
	else:
		logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""   )
	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."""   )
	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






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



