

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_     =			logging.get_logger(__name__)

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a=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:
		SCREAMING_SNAKE_CASE      :					str    =				os.path.abspath(_a)
		logger.info(f"Loading PyTorch weights from {pt_path}")

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

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

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a							,	):

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

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

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

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

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

	# conv layer
	SCREAMING_SNAKE_CASE      :					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(_a):
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				pt_tensor.transpose(2							,	3							,	1							,	0)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pt_tuple_key[:-1] + ("kernel",)
	if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_a):
		SCREAMING_SNAKE_CASE      :					Tuple    =				pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

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

	return pt_tuple_key, pt_tensor

def        lowerCamelCase__       (    _a							,	_a):
	# convert pytorch tensor to numpy
	SCREAMING_SNAKE_CASE      :					int    =				{k: v.numpy() for k, v in pt_state_dict.items()}

	SCREAMING_SNAKE_CASE      :					List[Any]    =				flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		SCREAMING_SNAKE_CASE      :					Dict    =				flax_model.params["params"]
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				flax_model.params
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				flatten_dict(_a)

	# add batch_stats keys,values to dict
	if "batch_stats" in flax_model.params:
		SCREAMING_SNAKE_CASE      :					int    =				flatten_dict(flax_model.params["batch_stats"])
		random_flax_state_dict.update(_a)

	SCREAMING_SNAKE_CASE      :					Dict    =				{}

	SCREAMING_SNAKE_CASE      :					int    =				(model_prefix not in flax_model_params) and (
	    model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
	)
	SCREAMING_SNAKE_CASE      :					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():
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				tuple(pt_key.split("."))

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

		# Correctly rename weight parameters
		SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					int    =				rename_key_and_reshape_tensor(
		    _a							,	_a							,	_a							,	_a)

		# add model prefix if necessary
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			SCREAMING_SNAKE_CASE      :					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] or "var" in flax_key[-1]:
				SCREAMING_SNAKE_CASE      :					Dict    =				jnp.asarray(_a)
				continue
			# remove num_batches_tracked key
			if "num_batches_tracked" in flax_key[-1]:
				flax_state_dict.pop(_a							,	_a)
				continue

			# also add unexpected weight so that warning is thrown
			SCREAMING_SNAKE_CASE      :					List[Any]    =				jnp.asarray(_a)

		else:
			# also add unexpected weight so that warning is thrown
			SCREAMING_SNAKE_CASE      :					str    =				jnp.asarray(_a)

	return unflatten_dict(_a)

def        lowerCamelCase__       (    _a							,	_a):
	import torch

	# Load the index
	SCREAMING_SNAKE_CASE      :					int    =				{}
	for shard_file in shard_filenames:
		# load using msgpack utils
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.load(_a)
		SCREAMING_SNAKE_CASE      :					Tuple    =				{k: v.numpy() for k, v in pt_state_dict.items()}

		SCREAMING_SNAKE_CASE      :					Tuple    =				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:
			SCREAMING_SNAKE_CASE      :					str    =				flax_model.params["params"]

			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				flatten_dict(_a)
			random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]))
		else:
			SCREAMING_SNAKE_CASE      :					Any    =				flax_model.params
			SCREAMING_SNAKE_CASE      :					Tuple    =				flatten_dict(_a)

		SCREAMING_SNAKE_CASE      :					List[Any]    =				(model_prefix not in flax_model_params) and (
		    model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
		)
		SCREAMING_SNAKE_CASE      :					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():
			SCREAMING_SNAKE_CASE      :					int    =				tuple(pt_key.split("."))

			# remove base model prefix if necessary
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				pt_tuple_key[0] == model_prefix
			if load_model_with_head_into_base_model and has_base_model_prefix:
				SCREAMING_SNAKE_CASE      :					Tuple    =				pt_tuple_key[1:]

			# Correctly rename weight parameters
			SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[int]    =				rename_key_and_reshape_tensor(
			    _a							,	_a							,	_a							,	_a)
			# add model prefix if necessary
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				SCREAMING_SNAKE_CASE      :					Optional[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]:
					SCREAMING_SNAKE_CASE      :					Dict    =				jnp.asarray(_a)
					continue
				if "var" in flax_key[-1]:
					SCREAMING_SNAKE_CASE      :					Tuple    =				jnp.asarray(_a)
					continue
				# remove num_batches_tracked key
				if "num_batches_tracked" in flax_key[-1]:
					flax_state_dict.pop(_a							,	_a)
					continue

				# also add unexpected weight so that warning is thrown
				SCREAMING_SNAKE_CASE      :					int    =				jnp.asarray(_a)

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

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					str    =				os.path.abspath(_a)
	logger.info(f"Loading Flax weights from {flax_checkpoint_path}")

	# import correct flax class
	SCREAMING_SNAKE_CASE      :					int    =				getattr(_a							,	"Flax" + model.__class__.__name__)

	# load flax weight dict
	with open(_a							,	"rb") as state_f:
		try:
			SCREAMING_SNAKE_CASE      :					str    =				from_bytes(_a							,	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(_a							,	_a)


def        lowerCamelCase__       (    _a							,	_a):

	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
	SCREAMING_SNAKE_CASE      :					int    =				flatten_dict(jax.tree_util.tree_map(lambda _a: x.dtype == jnp.bfloataa							,	_a)).values()
	if any(_a):
		# 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.")
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				jax.tree_util.tree_map(
		    lambda _a: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params							,	_a)

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				flatten_dict(_a)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				pt_model.state_dict()

	SCREAMING_SNAKE_CASE      :					Union[str, 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()}
	)
	SCREAMING_SNAKE_CASE      :					Dict    =				(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
	SCREAMING_SNAKE_CASE      :					int    =				[]
	SCREAMING_SNAKE_CASE      :					int    =				set(pt_model_dict.keys())

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

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

		if "batch_stats" in flax_state:
			SCREAMING_SNAKE_CASE      :					Dict    =				".".join(flax_key_tuple[1:])  # Remove the params/batch_stats header
		else:
			SCREAMING_SNAKE_CASE      :					List[str]    =				".".join(_a)

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

		if flax_key in special_pt_names:
			SCREAMING_SNAKE_CASE      :					Optional[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
				SCREAMING_SNAKE_CASE      :					Dict    =				np.asarray(_a) if not isinstance(_a							,	np.ndarray) else flax_tensor
				SCREAMING_SNAKE_CASE      :					int    =				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
	SCREAMING_SNAKE_CASE      :					str    =				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).")
	else:
		logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
	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.")
	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
from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase  							=						TypeVar('''T''')



class   snake_case_       (		Generic[T]       ):
   __A	:		deque[T]  # Cache store of keys
   __A	:		set[T]  # References of the keys in cache
   __A	:		int         =				10  # Maximum capacity of cache






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int  )					->						None:
       lowercase__ :   int    					=				deque()
       lowercase__ :   str    					=				set()
       if not n:
           lowercase__ :   str    					=				sys.maxsize
       elif n < 0:
           raise ValueError("n should be an integer greater than 0."  )
       else:
           lowercase__ :   List[Any]    					=				n






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     T  )					->						None:
       if x not in self.key_reference:
           if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
               lowercase__ :   Dict    					=				self.dq_store.pop()
               self.key_reference.remove(lowercase_  )
       else:
           self.dq_store.remove(lowercase_  )

       self.dq_store.appendleft(lowercase_  )
       self.key_reference.add(lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						None:
       for k in self.dq_store:
           print(lowercase_  )







   def __repr__(      self				:     Optional[int]  )					->						str:
       return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase  							=						LRUCache(4)
      lru_cache.refer('''A''')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('''A''')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



