





'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple     ):



    '''simple docstring'''





    try:
        with open(SCREAMING_SNAKE_CASE_		,							"rb"     ) as flax_state_f:
            _lowerCAmelCase						      =   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_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



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

    _lowerCAmelCase						      =   ""

    _lowerCAmelCase						      =   flatten_dict(SCREAMING_SNAKE_CASE_		,							sep="."     )
    _lowerCAmelCase						      =   pt_model.state_dict()

    # 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.split("."     )

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            _lowerCAmelCase						      =   flax_key_tuple_array[:-1] + ["weight"]
            _lowerCAmelCase						      =   jnp.transpose(SCREAMING_SNAKE_CASE_		,							(3, 2, 0, 1)     )
        elif flax_key_tuple_array[-1] == "kernel":
            _lowerCAmelCase						      =   flax_key_tuple_array[:-1] + ["weight"]
            _lowerCAmelCase						      =   flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            _lowerCAmelCase						      =   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						      =   (
                    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						      =   ".".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						      =   np.asarray(SCREAMING_SNAKE_CASE_     ) if not isinstance(SCREAMING_SNAKE_CASE_		,							np.ndarray     ) else flax_tensor
                _lowerCAmelCase						      =   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						      =   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









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       ["pixel_values"]




 def __init__(						self      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 1 / 255      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = IMAGENET_DEFAULT_MEAN      ,  _lowerCAmelCase = IMAGENET_DEFAULT_STD      ,  **_lowerCAmelCase      ,  )     ->				None:
     super().__init__(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
         _lowerCAmelCase						      =   int((256 / 224) * size["shortest_edge"]		)
         _lowerCAmelCase						      =   get_resize_output_image_size(_lowerCAmelCase      ,  size=_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
         _lowerCAmelCase						      =   {"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(
         _lowerCAmelCase      ,  size=(size_dict["height"], size_dict["width"])      ,  resample=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase		)
     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(_lowerCAmelCase      ,  size=(size["height"], size["width"])      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return rescale(_lowerCAmelCase      ,  scale=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return normalize(_lowerCAmelCase      ,  mean=_lowerCAmelCase      ,  std=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = ChannelDimension.FIRST      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase						      =   resample if resample is not None else self.resample
     _lowerCAmelCase						      =   do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase						      =   do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase						      =   rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase						      =   do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase						      =   image_std if image_std is not None else self.image_std

     _lowerCAmelCase						      =   size if size is not None else self.size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   make_list_of_images(_lowerCAmelCase		)

     if not valid_images(_lowerCAmelCase		):
         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.
     _lowerCAmelCase						      =   [to_numpy_array(_lowerCAmelCase		) for image in images]

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

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

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

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

     _lowerCAmelCase						      =   [to_channel_dimension_format(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase      ,  tensor_type=_lowerCAmelCase		)


