
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


UpperCamelCase_						=						logging.get_logger(__name__)





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

								A  :	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."""		)

								A  :	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
								A  :	Tuple				=					convert_pytorch_sharded_state_dict_to_flax(_SCREAMING_SNAKE_CASE					,						_SCREAMING_SNAKE_CASE		)
				return flax_state_dict





def 				_lowerCamelCase      (       lowerCamelCase_:	Tuple[str]					,						lowerCamelCase_:	np.ndarray					,						lowerCamelCase_:	Dict[str, jnp.ndarray]					,						lowerCamelCase_:	str					,						):



				'''simple docstring'''







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

				# layer norm
				A  :	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
				A  :	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
				A  :	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
				A  :	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
				A  :	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		):
								A  :	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(_SCREAMING_SNAKE_CASE		):
								A  :	Dict				=					pt_tensor.T
								return renamed_pt_tuple_key, pt_tensor

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

				return pt_tuple_key, pt_tensor





def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	Tuple		):



				'''simple docstring'''







				A  :	str				=					{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
				if "params" in flax_model.params:
								A  :	List[str]				=					flax_model.params["""params"""]
				else:
								A  :	List[str]				=					flax_model.params
				A  :	str				=					flatten_dict(_SCREAMING_SNAKE_CASE		)

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

				A  :	Any				=					{}

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

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

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

								# add model prefix if necessary
								A  :	int				=					(model_prefix,) + flax_key in random_flax_state_dict
								if load_base_model_into_model_with_head and require_base_model_prefix:
												A  :	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]:
																A  :	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
												A  :	Tuple				=					jnp.asarray(_SCREAMING_SNAKE_CASE		)

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

				return unflatten_dict(_SCREAMING_SNAKE_CASE		)





def 				_lowerCamelCase      (       lowerCamelCase_:	List[Any]					,						lowerCamelCase_:	List[str]		):



				'''simple docstring'''







				import torch

				# Load the index
				A  :	str				=					{}
				for shard_file in shard_filenames:
								# load using msgpack utils
								A  :	Any				=					torch.load(_SCREAMING_SNAKE_CASE		)
								A  :	Union[str, Any]				=					{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  :	Tuple				=					flax_model.params["""params"""]

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

								A  :	Optional[int]				=					(model_prefix not in flax_model_params) and (
								    model_prefix in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
								)
								A  :	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():
												A  :	str				=					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  :	Tuple				=					pt_tuple_key[1:]

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

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





def 				_lowerCamelCase      (       lowerCamelCase_:	Tuple					,						lowerCamelCase_:	List[str]		):



				'''simple docstring'''







				A  :	Optional[Any]				=					os.path.abspath(_SCREAMING_SNAKE_CASE		)
				logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""		)

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

				# load flax weight dict
				with open(_SCREAMING_SNAKE_CASE					,						'''rb'''		) as state_f:
								try:
												A  :	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      (       lowerCamelCase_:	str					,						lowerCamelCase_:	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
				A  :	int				=					flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase_		: 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.'''		)
								A  :	str				=					jax.tree_util.tree_map(
								    lambda lowerCamelCase_		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params					,						_SCREAMING_SNAKE_CASE		)

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

				A  :	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()}
				)
				A  :	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
				A  :	Any				=					[]
				A  :	str				=					set(pt_model_dict.keys()		)

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								A  :	List[str]				=					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[Any]				=					flax_key_tuple[1:]
								elif load_base_model_into_model_with_head and require_base_model_prefix:
												A  :	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
												A  :	Any				=					flax_key_tuple[:-1] + ("""weight""",)
												A  :	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
												A  :	List[Any]				=					flax_key_tuple[:-1] + ("""weight""",)
												A  :	List[str]				=					flax_tensor.T
								elif flax_key_tuple[-1] in ["scale", "embedding"]:
												A  :	Optional[int]				=					flax_key_tuple[:-1] + ("""weight""",)

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

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

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

								if flax_key in special_pt_names:
												A  :	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  :	str				=					np.asarray(_SCREAMING_SNAKE_CASE		) if not isinstance(_SCREAMING_SNAKE_CASE					,						np.ndarray		) else flax_tensor
																A  :	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
				A  :	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]						)			,     )						)






