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 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Dict=False	)  ->						Any:
					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(__lowerCamelCase	)
										logger.info(f"""Loading PyTorch weights from {pt_path}"""	)

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






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

					def is_key_or_prefix_key_in_dict(__lowerCamelCase	:    Tuple[str]	) -> bool:
										return len(set(__lowerCamelCase	) & {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(__lowerCamelCase	):
										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(__lowerCamelCase	):
										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(__lowerCamelCase	):
										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(__lowerCamelCase	):
										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(__lowerCamelCase	):
										__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(__lowerCamelCase	):
										__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 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    Tuple	)  ->						List[Any]:
					# convert pytorch tensor to numpy
					__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(__lowerCamelCase	)

					# 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(__lowerCamelCase	)

					__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   ,	__lowerCAmelCase	   =rename_key_and_reshape_tensor(
										    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

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

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

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

					return unflatten_dict(__lowerCamelCase	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    List[str]	)  ->						Dict:
					import torch

					# Load the index
					__lowerCAmelCase	   ={}
					for shard_file in shard_filenames:
										# load using msgpack utils
										__lowerCAmelCase	   =torch.load(__lowerCamelCase	)
										__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(__lowerCamelCase	)
															random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]	)	)
										else:
															__lowerCAmelCase	   =flax_model.params
															__lowerCAmelCase	   =flatten_dict(__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   ,	__lowerCAmelCase	   =rename_key_and_reshape_tensor(
															    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
															# 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(__lowerCamelCase	)
																									continue
																				if "var" in flax_key[-1]:
																									__lowerCAmelCase	   =jnp.asarray(__lowerCamelCase	)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(__lowerCamelCase				,   __lowerCamelCase	)
																									continue

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

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    List[str]	)  ->						str:
					__lowerCAmelCase	   =os.path.abspath(__lowerCamelCase	)
					logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""	)

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

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Tuple	)  ->						Any:

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

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

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

					pt_model.load_state_dict(__lowerCamelCase	)

					# re-transform missing_keys to list
					__lowerCAmelCase	   =list(__lowerCamelCase	)

					if len(__lowerCamelCase	) > 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(__lowerCamelCase	) > 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 PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
				lowercase_	       =			{
				    '''linear''': PIL.Image.Resampling.BILINEAR,
				    '''bilinear''': PIL.Image.Resampling.BILINEAR,
				    '''bicubic''': PIL.Image.Resampling.BICUBIC,
				    '''lanczos''': PIL.Image.Resampling.LANCZOS,
				    '''nearest''': PIL.Image.Resampling.NEAREST,
				}
else:
				lowercase_	       =			{
				    '''linear''': PIL.Image.LINEAR,
				    '''bilinear''': PIL.Image.BILINEAR,
				    '''bicubic''': PIL.Image.BICUBIC,
				    '''lanczos''': PIL.Image.LANCZOS,
				    '''nearest''': PIL.Image.NEAREST,
				}






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						int:
					__lowerCAmelCase	   =(images / 2 + 0.5).clamp(0				,   1	)
					__lowerCAmelCase	   =images.cpu().permute(0				,   2				,   3				,   1	).float().numpy()
					__lowerCAmelCase	   =numpy_to_pil(__lowerCamelCase	)
					return images






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					if images.ndim == 3:
										__lowerCAmelCase	   =images[None, ...]
					__lowerCAmelCase	   =(images * 255).round().astype("""uint8"""	)
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										__lowerCAmelCase	   =[Image.fromarray(image.squeeze()				,   mode="""L"""	) for image in images]
					else:
										__lowerCAmelCase	   =[Image.fromarray(__lowerCamelCase	) for image in images]

					return pil_images




