'''simple docstring'''




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          =  logging.get_logger(__name__)






def 				snake_case__							(			_A:					Any      ,   _A:					int      ,   _A:					Optional[Any]      ,   _A:					Dict=False )    ->	int:

		'''simple docstring'''


		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(_UpperCamelCase )
				logger.info(f"Loading PyTorch weights from {pt_path}" )

				lowerCAmelCase   					=  torch.load(_UpperCamelCase      ,   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(_UpperCamelCase      ,   _UpperCamelCase )
		else:
				# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
				lowerCAmelCase   					=  convert_pytorch_sharded_state_dict_to_flax(_UpperCamelCase      ,   _UpperCamelCase )
		return flax_state_dict






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

		'''simple docstring'''


		def is_key_or_prefix_key_in_dict(_A:					Tuple[str] ) -> bool:
				return len(set(_UpperCamelCase ) & {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(_UpperCamelCase ):
				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(_UpperCamelCase ):
				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(_UpperCamelCase ):
				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(_UpperCamelCase ):
				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(_UpperCamelCase ):
				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(_UpperCamelCase ):
				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 				snake_case__							(			_A:					Optional[Any]      ,   _A:					List[str] )    ->	str:

		'''simple docstring'''


		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(_UpperCamelCase )

		# 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(_UpperCamelCase )

		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(
				    _UpperCamelCase      ,   _UpperCamelCase      ,   _UpperCamelCase      ,   _UpperCamelCase )

				# 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(_UpperCamelCase )
								continue
						# remove num_batches_tracked key
						if "num_batches_tracked" in flax_key[-1]:
								flax_state_dict.pop(_UpperCamelCase      ,   _UpperCamelCase )
								continue

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

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

		return unflatten_dict(_UpperCamelCase )






def 				snake_case__							(			_A:					Tuple      ,   _A:					List[str] )    ->	Any:

		'''simple docstring'''


		import torch

		# Load the index
		lowerCAmelCase   					=  {}
		for shard_file in shard_filenames:
				# load using msgpack utils
				lowerCAmelCase   					=  torch.load(_UpperCamelCase )
				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(_UpperCamelCase )
						random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
				else:
						lowerCAmelCase   					=  flax_model.params
						lowerCAmelCase   					=  flatten_dict(_UpperCamelCase )

				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(
						    _UpperCamelCase      ,   _UpperCamelCase      ,   _UpperCamelCase      ,   _UpperCamelCase )
						# 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(_UpperCamelCase )
										continue
								if "var" in flax_key[-1]:
										lowerCAmelCase   					=  jnp.asarray(_UpperCamelCase )
										continue
								# remove num_batches_tracked key
								if "num_batches_tracked" in flax_key[-1]:
										flax_state_dict.pop(_UpperCamelCase      ,   _UpperCamelCase )
										continue

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

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






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					int )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  os.path.abspath(_UpperCamelCase )
		logger.info(f"Loading Flax weights from {flax_checkpoint_path}" )

		# import correct flax class
		lowerCAmelCase   					=  getattr(_UpperCamelCase      ,   """Flax""" + model.__class__.__name__ )

		# load flax weight dict
		with open(_UpperCamelCase      ,   """rb""" ) as state_f:
				try:
						lowerCAmelCase   					=  from_bytes(_UpperCamelCase      ,   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(_UpperCamelCase      ,   _UpperCamelCase )






def 				snake_case__							(			_A:					List[Any]      ,   _A:					int )    ->	List[str]:

		'''simple docstring'''


		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 _A : x.dtype == jnp.bfloataa      ,   _UpperCamelCase ) ).values()
		if any(_UpperCamelCase ):
				# 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 _A : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params      ,   _UpperCamelCase )

		lowerCAmelCase   					=  flatten_dict(_UpperCamelCase )
		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(_UpperCamelCase ) not in pt_model_dict:
						# conv layer
						lowerCAmelCase   					=  flax_key_tuple[:-1] + ("""weight""",)
						lowerCAmelCase   					=  jnp.transpose(_UpperCamelCase      ,   (3, 2, 0, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCamelCase ) 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(_UpperCamelCase )

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

		pt_model.load_state_dict(_UpperCamelCase )

		# re-transform missing_keys to list
		lowerCAmelCase   					=  list(_UpperCamelCase )

		if len(_UpperCamelCase ) > 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(_UpperCamelCase ) > 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








"""simple docstring"""






from collections.abc import Callable
def      lowerCamelCase       (     _UpperCamelCase		:      Callable[[float], float]       ,			_UpperCamelCase		:      float       ,			_UpperCamelCase		:      float							)		->       float:

							'''simple docstring'''



							__UpperCAmelCase   :						float      							=					a
							__UpperCAmelCase   :						float      							=					b
							if function(_UpperCamelCase							) == 0:  # one of the a or b is a root for the function
														return a
							elif function(_UpperCamelCase							) == 0:
														return b
							elif (
							    function(_UpperCamelCase							) * function(_UpperCamelCase							) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval."""							)
							else:
														__UpperCAmelCase   :						float      							=					start + (end - start) / 2.0
														while abs(start - mid							) > 1_0**-7:  # until precisely equals to 10^-7
																					if function(_UpperCamelCase							) == 0:
																												return mid
																					elif function(_UpperCamelCase							) * function(_UpperCamelCase							) < 0:
																												__UpperCAmelCase   :						Union[str, Any]      							=					mid
																					else:
																												__UpperCAmelCase   :						List[Any]      							=					mid
																					__UpperCAmelCase   :						Optional[Any]      							=					start + (end - start) / 2.0
														return mid
def      lowerCamelCase       (     _UpperCamelCase		:      float							)		->       float:

							'''simple docstring'''



							return x**3 - 2 * x - 5


if __name__ == "__main__":
							print(bisection(f, 1, 1000))

							import doctest

							doctest.testmod()
