


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


lowerCAmelCase       =							logging.get_logger(__name__)


class        _a							(			UpperCamelCase__			):
							_lowercase	:			Union[str, Any]        = ['''pixel_values''']

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Dict[str, int] = None    ,						UpperCamelCase_:	PILImageResampling = PILImageResampling.BICUBIC    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Dict[str, int] = None    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Union[int, float] = 1 / 255    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN    ,						UpperCamelCase_:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD    ,						**UpperCamelCase_:	str    ,						)			->   None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__            =      size if size is not None else {'''shortest_edge''': 224}
									lowercase__            =      get_size_dict(UpperCamelCase_    ,						default_to_square=UpperCamelCase_							)
									lowercase__            =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									lowercase__            =      get_size_dict(UpperCamelCase_    ,						param_name='''crop_size'''							)

									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      resample
									lowercase__            =      do_center_crop
									lowercase__            =      crop_size
									lowercase__            =      do_rescale
									lowercase__            =      rescale_factor
									lowercase__            =      do_normalize
									lowercase__            =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									lowercase__            =      image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	np.ndarray    ,						UpperCamelCase_:	Dict[str, int]    ,						UpperCamelCase_:	PILImageResampling = PILImageResampling.BICUBIC    ,						UpperCamelCase_:	Optional[Union[str, ChannelDimension]] = None    ,						**UpperCamelCase_:	List[Any]    ,						)			->   np.ndarray:





									"""simple docstring"""







									lowercase__            =      get_size_dict(UpperCamelCase_    ,						default_to_square=UpperCamelCase_							)
									# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
									if "shortest_edge" in size:
											lowercase__            =      int((256 / 224) * size['''shortest_edge''']							)
											lowercase__            =      get_resize_output_image_size(UpperCamelCase_    ,						size=UpperCamelCase_    ,						default_to_square=UpperCamelCase_							)
											lowercase__            =      {'''height''': output_size[0], '''width''': output_size[1]}
									if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'							)
									return resize(
									    UpperCamelCase_    ,						size=(size_dict['''height'''], size_dict['''width'''])    ,						resample=UpperCamelCase_    ,						data_format=UpperCamelCase_    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	np.ndarray    ,						UpperCamelCase_:	Dict[str, int]    ,						UpperCamelCase_:	Optional[Union[str, ChannelDimension]] = None    ,						**UpperCamelCase_:	Any    ,						)			->   np.ndarray:





									"""simple docstring"""







									lowercase__            =      get_size_dict(UpperCamelCase_							)
									if "height" not in size or "width" not in size:
											raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'							)
									return center_crop(UpperCamelCase_    ,						size=(size['''height'''], size['''width'''])    ,						data_format=UpperCamelCase_    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	np.ndarray    ,						UpperCamelCase_:	Union[int, float]    ,						UpperCamelCase_:	Optional[Union[str, ChannelDimension]] = None    ,						**UpperCamelCase_:	str    ,						)			->   np.ndarray:





									"""simple docstring"""







									return rescale(UpperCamelCase_    ,						scale=UpperCamelCase_    ,						data_format=UpperCamelCase_    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	np.ndarray    ,						UpperCamelCase_:	Union[float, List[float]]    ,						UpperCamelCase_:	Union[float, List[float]]    ,						UpperCamelCase_:	Optional[Union[str, ChannelDimension]] = None    ,						**UpperCamelCase_:	int    ,						)			->   np.ndarray:





									"""simple docstring"""







									return normalize(UpperCamelCase_    ,						mean=UpperCamelCase_    ,						std=UpperCamelCase_    ,						data_format=UpperCamelCase_    ,						**UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	ImageInput    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[Dict[str, int]] = None    ,						UpperCamelCase_:	PILImageResampling = None    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[Dict[str, int]] = None    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[float] = None    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[Union[float, Iterable[float]]] = None    ,						UpperCamelCase_:	Optional[Union[float, Iterable[float]]] = None    ,						UpperCamelCase_:	Optional[TensorType] = None    ,						UpperCamelCase_:	ChannelDimension = ChannelDimension.FIRST    ,						**UpperCamelCase_:	str    ,						)			->   BatchFeature:





									"""simple docstring"""







									lowercase__            =      do_resize if do_resize is not None else self.do_resize
									lowercase__            =      resample if resample is not None else self.resample
									lowercase__            =      do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase__            =      do_rescale if do_rescale is not None else self.do_rescale
									lowercase__            =      rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__            =      do_normalize if do_normalize is not None else self.do_normalize
									lowercase__            =      image_mean if image_mean is not None else self.image_mean
									lowercase__            =      image_std if image_std is not None else self.image_std

									lowercase__            =      size if size is not None else self.size
									lowercase__            =      get_size_dict(UpperCamelCase_    ,						default_to_square=UpperCamelCase_							)
									lowercase__            =      crop_size if crop_size is not None else self.crop_size
									lowercase__            =      get_size_dict(UpperCamelCase_    ,						param_name='''crop_size'''							)

									lowercase__            =      make_list_of_images(UpperCamelCase_							)

									if not valid_images(UpperCamelCase_							):
											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:
											raise ValueError('''Size 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.
									lowercase__            =      [to_numpy_array(UpperCamelCase_							) for image in images]

									if do_resize:
											lowercase__            =      [self.resize(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									if do_center_crop:
											lowercase__            =      [self.center_crop(UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									if do_rescale:
											lowercase__            =      [self.rescale(UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									if do_normalize:
											lowercase__            =      [self.normalize(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									lowercase__            =      [to_channel_dimension_format(UpperCamelCase_    ,						UpperCamelCase_							) for image in images]

									lowercase__            =      {'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase_    ,						tensor_type=UpperCamelCase_							)






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


lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as flax_state_f:
						lowercase__            =      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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""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
		lowercase__            =      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.'''     )
				lowercase__            =      jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params						,				SCREAMING_SNAKE_CASE     )

		lowercase__            =      ''''''

		lowercase__            =      flatten_dict(SCREAMING_SNAKE_CASE						,				sep='''.'''     )
		lowercase__            =      pt_model.state_dict()

		# 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.split('''.'''     )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      jnp.transpose(SCREAMING_SNAKE_CASE						,				(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						lowercase__            =      flax_key_tuple_array[:-1] + ['''weight''']
						lowercase__            =      flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						lowercase__            =      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     ):
								lowercase__            =      (
								    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'''     )
								)

				lowercase__            =      '''.'''.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
								lowercase__            =      np.asarray(SCREAMING_SNAKE_CASE     ) if not isinstance(SCREAMING_SNAKE_CASE						,				np.ndarray     ) else flax_tensor
								lowercase__            =      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
		lowercase__            =      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


