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


SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[int]=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:
												UpperCamelCase_		:							int  =		os.path.abspath(_SCREAMING_SNAKE_CASE			)
												logger.info(f'''Loading PyTorch weights from {pt_path}'''			)

												UpperCamelCase_		:							int  =		torch.load(_SCREAMING_SNAKE_CASE		, map_location="""cpu"""			)
												logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters.'''			)

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple[str]		, _SCREAMING_SNAKE_CASE     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				Dict[str, jnp.ndarray]		, _SCREAMING_SNAKE_CASE     :				str		, ):

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

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

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

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

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

						# conv layer
						UpperCamelCase_		:							Optional[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(_SCREAMING_SNAKE_CASE			):
												UpperCamelCase_		:							Any  =		pt_tensor.transpose(2		, 3		, 1		, 0			)
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						return pt_tuple_key, pt_tensor






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				Tuple			):
						# convert pytorch tensor to numpy
						UpperCamelCase_		:							str  =		{k: v.numpy() for k, v in pt_state_dict.items()}

						UpperCamelCase_		:							Optional[Any]  =		flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												UpperCamelCase_		:							List[str]  =		flax_model.params["""params"""]
						else:
												UpperCamelCase_		:							List[str]  =		flax_model.params
						UpperCamelCase_		:							str  =		flatten_dict(_SCREAMING_SNAKE_CASE			)

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												UpperCamelCase_		:							Optional[Any]  =		flatten_dict(flax_model.params["""batch_stats"""]			)
												random_flax_state_dict.update(_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Any  =		{}

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

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

												# Correctly rename weight parameters
												UpperCamelCase_,UpperCamelCase_		:							Dict  =		rename_key_and_reshape_tensor(
												    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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

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

												else:
																		# also add unexpected weight so that warning is thrown
																		UpperCamelCase_		:							Dict  =		jnp.asarray(_SCREAMING_SNAKE_CASE			)

						return unflatten_dict(_SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						import torch

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

												UpperCamelCase_		:							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:
																		UpperCamelCase_		:							Tuple  =		flax_model.params["""params"""]

																		UpperCamelCase_		:							Optional[int]  =		flatten_dict(_SCREAMING_SNAKE_CASE			)
																		random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]			)			)
												else:
																		UpperCamelCase_		:							Tuple  =		flax_model.params
																		UpperCamelCase_		:							Tuple  =		flatten_dict(_SCREAMING_SNAKE_CASE			)

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

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

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

																								# also add unexpected weight so that warning is thrown
																								UpperCamelCase_		:							List[str]  =		jnp.asarray(_SCREAMING_SNAKE_CASE			)

																		else:
																								# also add unexpected weight so that warning is thrown
																								UpperCamelCase_		:							Optional[Any]  =		jnp.asarray(_SCREAMING_SNAKE_CASE			)
						return unflatten_dict(_SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Optional[Any]  =		os.path.abspath(_SCREAMING_SNAKE_CASE			)
						logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''			)

						# import correct flax class
						UpperCamelCase_		:							Optional[int]  =		getattr(_SCREAMING_SNAKE_CASE		, """Flax""" + model.__class__.__name__			)

						# load flax weight dict
						with open(_SCREAMING_SNAKE_CASE		, """rb"""			) as state_f:
												try:
																		UpperCamelCase_		:							int  =		from_bytes(_SCREAMING_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(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)







def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str			):

						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
						UpperCamelCase_		:							int  =		flatten_dict(jax.tree_util.tree_map(lambda _SCREAMING_SNAKE_CASE			: x.dtype == jnp.bfloataa		, _SCREAMING_SNAKE_CASE			)			).values()
						if any(_SCREAMING_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."""			)
												UpperCamelCase_		:							str  =		jax.tree_util.tree_map(
												    lambda _SCREAMING_SNAKE_CASE			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params		, _SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Union[str, Any]  =		flatten_dict(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							int  =		pt_model.state_dict()

						UpperCamelCase_		:							Optional[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()}
						)
						UpperCamelCase_		:							Tuple  =		(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
						UpperCamelCase_		:							Any  =		[]
						UpperCamelCase_		:							str  =		set(pt_model_dict.keys()			)

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

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

												if "batch_stats" in flax_state:
																		UpperCamelCase_		:							Union[str, Any]  =		""".""".join(flax_key_tuple[1:]			)  # Remove the params/batch_stats header
												else:
																		UpperCamelCase_		:							Dict  =		""".""".join(_SCREAMING_SNAKE_CASE			)

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

												if flax_key in special_pt_names:
																		UpperCamelCase_		:							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
																								UpperCamelCase_		:							str  =		np.asarray(_SCREAMING_SNAKE_CASE			) if not isinstance(_SCREAMING_SNAKE_CASE		, np.ndarray			) else flax_tensor
																								UpperCamelCase_		:							Tuple  =		torch.from_numpy(_SCREAMING_SNAKE_CASE			)
																								# remove from missing keys
																								missing_keys.remove(_SCREAMING_SNAKE_CASE			)
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(_SCREAMING_SNAKE_CASE			)

						pt_model.load_state_dict(_SCREAMING_SNAKE_CASE			)

						# re-transform missing_keys to list
						UpperCamelCase_		:							Any  =		list(_SCREAMING_SNAKE_CASE			)

						if len(_SCREAMING_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(_SCREAMING_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 unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






