"""simple docstring"""






import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


__lowerCAmelCase					: Optional[List[str]]        =None
__lowerCAmelCase					: str        ="""<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCAmelCase					: Union[str, Any]        =[
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]





@dataclass
class 				_A		:
      snake_case__    :     bool													=			True
      snake_case__    :     Optional[str]													=			None
      # Automatically constructed
      snake_case__    :     ClassVar[str]													=			"PIL.Image.Image"
      snake_case__    :     ClassVar[Any]													=			pa.struct({'bytes': pa.binary(), 'path': pa.string()}			)
      snake_case__    :     str													=			field(default='Image'						,					init=lowerCAmelCase						,					repr=lowerCAmelCase			)

      def __call__(		self		):







            """simple docstring"""



            return self.pa_type

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            if config.PIL_AVAILABLE:
                  import PIL.Image
            else:
                  raise ImportError("""To support encoding images, please install 'Pillow'."""		)

            if isinstance(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			np.array(__lowerCAmelCase		)

            if isinstance(__lowerCAmelCase	, __lowerCAmelCase		):
                  return {"path": value, "bytes": None}
            elif isinstance(__lowerCAmelCase	, __lowerCAmelCase		):
                  return {"path": None, "bytes": value}
            elif isinstance(__lowerCAmelCase	, np.ndarray		):
                  # convert the image array to PNG/TIFF bytes
                  return encode_np_array(__lowerCAmelCase		)
            elif isinstance(__lowerCAmelCase	, PIL.Image.Image		):
                  # convert the PIL image to bytes (default format is PNG/TIFF)
                  return encode_pil_image(__lowerCAmelCase		)
            elif value.get("""path"""		) is not None and os.path.isfile(value["""path"""]		):
                  # we set "bytes": None to not duplicate the data if they're already available locally
                  return {"bytes": None, "path": value.get("""path"""		)}
            elif value.get("""bytes"""		) is not None or value.get("""path"""		) is not None:
                  # store the image bytes, and path is used to infer the image format using the file extension
                  return {"bytes": value.get("""bytes"""		), "path": value.get("""path"""		)}
            else:
                  raise ValueError(
                      f'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



            if not self.decode:
                  raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""		)

            if config.PIL_AVAILABLE:
                  import PIL.Image
            else:
                  raise ImportError("""To support decoding images, please install 'Pillow'."""		)

            if token_per_repo_id is None:
                  lowercase														=			{}

            lowercase							,							lowercase														=			value["""path"""], value["""bytes"""]
            if bytes_ is None:
                  if path is None:
                        raise ValueError(f'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'		)
                  else:
                        if is_local_path(__lowerCAmelCase		):
                              lowercase														=			PIL.Image.open(__lowerCAmelCase		)
                        else:
                              lowercase														=			path.split("""::"""		)[-1]
                              try:
                                    lowercase														=			string_to_dict(__lowerCAmelCase	, config.HUB_DATASETS_URL		)["""repo_id"""]
                                    lowercase														=			token_per_repo_id.get(__lowerCAmelCase		)
                              except ValueError:
                                    lowercase														=			None
                              with xopen(__lowerCAmelCase	, """rb"""	, use_auth_token=__lowerCAmelCase		) as f:
                                    lowercase														=			BytesIO(f.read()		)
                              lowercase														=			PIL.Image.open(bytes_		)
            else:
                  lowercase														=			PIL.Image.open(BytesIO(bytes_		)		)
            image.load()  # to avoid "Too many open files" errors
            return image

      def        A__ (		self		):







            """simple docstring"""



            from .features import Value

            return (
                self
                if self.decode
                else {
                    "bytes": Value("""binary"""		),
                    "path": Value("""string"""		),
                }
            )

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            if pa.types.is_string(storage.type		):
                  lowercase														=			pa.array([None] * len(__lowerCAmelCase		)	, type=pa.binary()		)
                  lowercase														=			pa.StructArray.from_arrays([bytes_array, storage]	, ["""bytes""", """path"""]	, mask=storage.is_null()		)
            elif pa.types.is_binary(storage.type		):
                  lowercase														=			pa.array([None] * len(__lowerCAmelCase		)	, type=pa.string()		)
                  lowercase														=			pa.StructArray.from_arrays([storage, path_array]	, ["""bytes""", """path"""]	, mask=storage.is_null()		)
            elif pa.types.is_struct(storage.type		):
                  if storage.type.get_field_index("""bytes"""		) >= 0:
                        lowercase														=			storage.field("""bytes"""		)
                  else:
                        lowercase														=			pa.array([None] * len(__lowerCAmelCase		)	, type=pa.binary()		)
                  if storage.type.get_field_index("""path"""		) >= 0:
                        lowercase														=			storage.field("""path"""		)
                  else:
                        lowercase														=			pa.array([None] * len(__lowerCAmelCase		)	, type=pa.string()		)
                  lowercase														=			pa.StructArray.from_arrays([bytes_array, path_array]	, ["""bytes""", """path"""]	, mask=storage.is_null()		)
            elif pa.types.is_list(storage.type		):
                  lowercase														=			pa.array(
                      [encode_np_array(np.array(__lowerCAmelCase		)		)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]	, type=pa.binary()	, )
                  lowercase														=			pa.array([None] * len(__lowerCAmelCase		)	, type=pa.string()		)
                  lowercase														=			pa.StructArray.from_arrays(
                      [bytes_array, path_array]	, ["""bytes""", """path"""]	, mask=bytes_array.is_null()		)
            return array_cast(__lowerCAmelCase	, self.pa_type		)







      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            @no_op_if_value_is_null
            def path_to_bytes(__lowerCAmelCase		):
                  with xopen(__lowerCAmelCase	, """rb"""		) as f:
                        lowercase														=			f.read()
                  return bytes_

            lowercase														=			pa.array(
                [
                    (path_to_bytes(x["""path"""]		) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
                    for x in storage.to_pylist()
                ]	, type=pa.binary()	, )
            lowercase														=			pa.array(
                [os.path.basename(__lowerCAmelCase		) if path is not None else None for path in storage.field("""path"""		).to_pylist()]	, type=pa.string()	, )
            lowercase														=			pa.StructArray.from_arrays([bytes_array, path_array]	, ["""bytes""", """path"""]	, mask=bytes_array.is_null()		)
            return array_cast(__lowerCAmelCase	, self.pa_type		)






def     UpperCAmelCase__ (       )				->					List[str]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""	)

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            lowercase														=			list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
      return _IMAGE_COMPRESSION_FORMATS


def     UpperCAmelCase__ (       lowerCAmelCase__  :"PIL.Image.Image"	)				->					bytes:
      '''simple docstring'''


      lowercase														=			BytesIO()
      if image.format in list_image_compression_formats():
            lowercase														=			image.format
      else:
            lowercase														=			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
      image.save(lowerCAmelCase__      ,	format=lowerCAmelCase__	)
      return buffer.getvalue()


def     UpperCAmelCase__ (       lowerCAmelCase__  :"PIL.Image.Image"	)				->					dict:
      '''simple docstring'''


      if hasattr(lowerCAmelCase__      ,	"""filename"""	) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(lowerCAmelCase__	)}


def     UpperCAmelCase__ (       lowerCAmelCase__  :np.ndarray	)				->					dict:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""	)

      lowercase														=			array.dtype
      lowercase														=			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
      lowercase														=			dtype.kind
      lowercase														=			dtype.itemsize

      lowercase														=			None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowercase														=			np.dtype("""|u1"""	)
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'	)
            if dtype is not dest_dtype:
                  warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''	)
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            lowercase														=			dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  lowercase														=			dtype_byteorder + dtype_kind + str(lowerCAmelCase__	)
                  lowercase														=			np.dtype(lowerCAmelCase__	)
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''	)
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'	)
      lowercase														=			PIL.Image.fromarray(array.astype(lowerCAmelCase__	)	)
      return {"path": None, "bytes": image_to_bytes(lowerCAmelCase__	)}






def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]	)				->					List[dict]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""	)

      if objs:
            lowercase							,							lowercase														=			first_non_null_value(lowerCAmelCase__	)
            if isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(lowerCAmelCase__      ,	np.ndarray	):
                  lowercase														=			no_op_if_value_is_null(lowerCAmelCase__	)
                  return [obj_to_image_dict_func(lowerCAmelCase__	) for obj in objs]
            elif isinstance(lowerCAmelCase__      ,	PIL.Image.Image	):
                  lowercase														=			no_op_if_value_is_null(lowerCAmelCase__	)
                  return [obj_to_image_dict_func(lowerCAmelCase__	) for obj in objs]
            else:
                  return objs
      else:
            return objs




"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase														=			key.replace("""heads.cmd.mim_head.cls.predictions"""      ,	"""mmm_image_head"""	)
            lowercase														=			key.replace("""heads.cmd.mlm_head.cls.predictions"""      ,	"""mmm_text_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.cls"""      ,	"""itm_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.pooler"""      ,	"""itm_head.pooler"""	)
            lowercase														=			key.replace("""heads.cmd.clip_head.logit_scale"""      ,	"""flava.logit_scale"""	)
            lowercase														=			key.replace("""heads.fairseq_mlm.cls.predictions"""      ,	"""mlm_head"""	)
            lowercase														=			key.replace("""heads.imagenet.mim_head.cls.predictions"""      ,	"""mim_head"""	)
            lowercase														=			key.replace("""mm_text_projection"""      ,	"""flava.text_to_mm_projection"""	)
            lowercase														=			key.replace("""mm_image_projection"""      ,	"""flava.image_to_mm_projection"""	)
            lowercase														=			key.replace("""image_encoder.module"""      ,	"""flava.image_model"""	)
            lowercase														=			key.replace("""text_encoder.module"""      ,	"""flava.text_model"""	)
            lowercase														=			key.replace("""mm_encoder.module.encoder.cls_token"""      ,	"""flava.multimodal_model.cls_token"""	)
            lowercase														=			key.replace("""mm_encoder.module"""      ,	"""flava.multimodal_model"""	)
            lowercase														=			key.replace("""text_projection"""      ,	"""flava.text_projection"""	)
            lowercase														=			key.replace("""image_projection"""      ,	"""flava.image_projection"""	)

            lowercase														=			value.float()

      for key, value in codebook_state_dict.items():
            lowercase														=			value

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

      if os.path.exists(lowerCAmelCase__	):
            lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      else:
            lowercase														=			torch.hub.load_state_dict_from_url(lowerCAmelCase__      ,	map_location="""cpu"""	)

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

      assert torch.allclose(lowerCAmelCase__      ,	lowerCAmelCase__      ,	atol=1e-3	)

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =argparse.ArgumentParser()
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
       parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)



