


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			:			List[Any]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=False	):


			'''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:
						__lowercase			       = os.path.abspath(_UpperCamelCase	)
						logger.info(F'Loading PyTorch weights from {pt_path}'	)

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

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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      ):


			'''simple docstring'''







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

			# layer norm
			__lowercase			       = 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
			__lowercase			       = 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
			__lowercase			       = 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
			__lowercase			       = 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
			__lowercase			       = 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	):
						__lowercase			       = pt_tensor.transpose(2							,      3							,      1							,      0	)
						return renamed_pt_tuple_key, pt_tensor

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

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

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

			return pt_tuple_key, pt_tensor


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {k: v.numpy() for k, v in pt_state_dict.items()}

			__lowercase			       = flax_model.base_model_prefix

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

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

			__lowercase			       = {}

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

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

						# Correctly rename weight parameters
						__lowercase       ,			__lowercase			       = rename_key_and_reshape_tensor(
						    _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)

						# add model prefix if necessary
						__lowercase			       = (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									__lowercase			       = (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]:
												__lowercase			       = 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
									__lowercase			       = jnp.asarray(_UpperCamelCase	)

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

			return unflatten_dict(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			import torch

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

						__lowercase			       = 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:
									__lowercase			       = flax_model.params['''params''']

									__lowercase			       = flatten_dict(_UpperCamelCase	)
									random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']	)	)
						else:
									__lowercase			       = flax_model.params
									__lowercase			       = flatten_dict(_UpperCamelCase	)

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

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

									# Correctly rename weight parameters
									__lowercase       ,			__lowercase			       = rename_key_and_reshape_tensor(
									    _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)
									# add model prefix if necessary
									__lowercase			       = (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												__lowercase			       = (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]:
															__lowercase			       = jnp.asarray(_UpperCamelCase	)
															continue
												if "var" in flax_key[-1]:
															__lowercase			       = 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
												__lowercase			       = jnp.asarray(_UpperCamelCase	)

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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = os.path.abspath(_UpperCamelCase	)
			logger.info(F'Loading Flax weights from {flax_checkpoint_path}'	)

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

			# load flax weight dict
			with open(_UpperCamelCase							,      '''rb'''	) as state_f:
						try:
									__lowercase			       = 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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''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
			__lowercase			       = flatten_dict(jax.tree_util.tree_map(lambda _UpperCamelCase	: 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.'''	)
						__lowercase			       = jax.tree_util.tree_map(
						    lambda _UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params							,      _UpperCamelCase	)

			__lowercase			       = flatten_dict(_UpperCamelCase	)
			__lowercase			       = pt_model.state_dict()

			__lowercase			       = (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()}
			)
			__lowercase			       = (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
			__lowercase			       = []
			__lowercase			       = set(pt_model_dict.keys()	)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						__lowercase			       = flax_key_tuple[0] == pt_model.base_model_prefix
						__lowercase			       = '''.'''.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:
									__lowercase			       = flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									__lowercase			       = (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
									__lowercase			       = flax_key_tuple[:-1] + ('''weight''',)
									__lowercase			       = jnp.transpose(_UpperCamelCase							,      (3, 2, 0, 1)	)
						elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCamelCase	) not in pt_model_dict:
									# linear layer
									__lowercase			       = flax_key_tuple[:-1] + ('''weight''',)
									__lowercase			       = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									__lowercase			       = flax_key_tuple[:-1] + ('''weight''',)

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

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

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

						if flax_key in special_pt_names:
									__lowercase			       = 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
												__lowercase			       = np.asarray(_UpperCamelCase	) if not isinstance(_UpperCamelCase							,      np.ndarray	) else flax_tensor
												__lowercase			       = 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
			__lowercase			       = 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






from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


