





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :np.ndarray						,    __lowercase  :Union[int, float]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :List[Any]						,    ):
											return rescale(__lowercase						,    scale=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

											# All transformations expect numpy arrays.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

											if do_rescale:
																		__lowerCamelCase      :      str         =[self.rescale(image=__lowercase						,    scale=__lowercase			) for image in images]

											if do_normalize:
																		__lowerCamelCase      :      int         =[self.normalize(image=__lowercase						,    mean=__lowercase						,    std=__lowercase			) for image in images]

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)











"""simple docstring"""





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							try:
														with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as flax_state_f:
																					__lowerCamelCase      :      int         =from_bytes(SCREAMING_SNAKE_CASE    ,						flax_state_f.read()   )
							except UnpicklingError as e:
														try:
																					with open(SCREAMING_SNAKE_CASE   ) as f:
																												if f.read().startswith('''version'''   ):
																																			raise OSError(
																																			    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																																			    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																																			    ''' folder you cloned.'''   )
																												else:
																																			raise ValueError from e
														except (UnicodeDecodeError, ValueError):
																					raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '   )

							return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    '''Loading 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      :      str         =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 they 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      :      List[str]         =jax.tree_util.tree_map(
														    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =''''''

							__lowerCamelCase      :      List[str]         =flatten_dict(SCREAMING_SNAKE_CASE    ,						sep='''.'''   )
							__lowerCamelCase      :      List[str]         =pt_model.state_dict()

							# keep track of unexpected & missing keys
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      int         =set(pt_model_dict.keys()   )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														__lowerCamelCase      :      Tuple         =flax_key_tuple.split('''.'''   )

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__lowerCamelCase      :      Optional[int]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Union[str, Any]         =jnp.transpose(SCREAMING_SNAKE_CASE    ,						(3, 2, 0, 1)   )
														elif flax_key_tuple_array[-1] == "kernel":
																					__lowerCamelCase      :      Optional[Any]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Dict         =flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__lowerCamelCase      :      str         =flax_key_tuple_array[:-1] + ['''weight''']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Union[str, Any]         =(
																												    flax_key_tuple_string.replace('''_0'''    ,						'''.0'''   )
																												    .replace('''_1'''    ,						'''.1'''   )
																												    .replace('''_2'''    ,						'''.2'''   )
																												    .replace('''_3'''    ,						'''.3'''   )
																												    .replace('''_4'''    ,						'''.4'''   )
																												    .replace('''_5'''    ,						'''.5'''   )
																												    .replace('''_6'''    ,						'''.6'''   )
																												    .replace('''_7'''    ,						'''.7'''   )
																												    .replace('''_8'''    ,						'''.8'''   )
																												    .replace('''_9'''    ,						'''.9'''   )
																												)

														__lowerCamelCase      :      Optional[int]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

														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      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   ) if not isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ) else flax_tensor
																												__lowerCamelCase      :      Any         =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
							__lowerCamelCase      :      Optional[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).'''   )
							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.'''   )

							return pt_model




