
'''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


a_											=						None
a_											=						"<" 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
a_											=						[
    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    UpperCAmelCase_				:
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						None
 # Automatically constructed
 UpperCAmelCase_		    =						"""PIL.Image.Image"""
 UpperCAmelCase_		    =						pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}					)
 UpperCAmelCase_		    =						field(default="""Image"""    ,						init=snake_case__    ,						repr=snake_case__					)





 def __call__(    self):
     return self.pa_type





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

     if isinstance(lowercase_ ,       lowercase_):
         snake_case_ :    str     			=						np.array(lowercase_)

     if isinstance(lowercase_ ,       lowercase_):
         return {"path": value, "bytes": None}
     elif isinstance(lowercase_ ,       lowercase_):
         return {"path": None, "bytes": value}
     elif isinstance(lowercase_ ,       np.ndarray):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(lowercase_)
     elif isinstance(lowercase_ ,       PIL.Image.Image):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(lowercase_)
     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        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     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:
         snake_case_ :    Dict     			=						{}

     snake_case_ :    Dict     			=						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(lowercase_):
                 snake_case_ :    Optional[int]     			=						PIL.Image.open(lowercase_)
             else:
                 snake_case_ :    str     			=						path.split("::")[-1]
                 try:
                     snake_case_ :    int     			=						string_to_dict(lowercase_ ,       config.HUB_DATASETS_URL)["repo_id"]
                     snake_case_ :    Optional[Any]     			=						token_per_repo_id.get(lowercase_)
                 except ValueError:
                     snake_case_ :    int     			=						None
                 with xopen(lowercase_ ,       "rb" ,       use_auth_token=lowercase_) as f:
                     snake_case_ :    List[str]     			=						BytesIO(f.read())
                 snake_case_ :    Dict     			=						PIL.Image.open(bytes_)
     else:
         snake_case_ :    Union[str, Any]     			=						PIL.Image.open(BytesIO(bytes_))
     image.load()  # to avoid "Too many open files" errors
     return image





 def        snake_case__		(    self):
     from .features import Value

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





 def        snake_case__		(    self ,       lowercase_):
     if pa.types.is_string(storage.type):
         snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.binary())
         snake_case_ :    int     			=						pa.StructArray.from_arrays([bytes_array, storage] ,       ["bytes", "path"] ,       mask=storage.is_null())
     elif pa.types.is_binary(storage.type):
         snake_case_ :    str     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Optional[int]     			=						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:
             snake_case_ :    List[str]     			=						storage.field("bytes")
         else:
             snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.binary())
         if storage.type.get_field_index("path") >= 0:
             snake_case_ :    Optional[Any]     			=						storage.field("path")
         else:
             snake_case_ :    str     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Dict     			=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ["bytes", "path"] ,       mask=storage.is_null())
     elif pa.types.is_list(storage.type):
         snake_case_ :    List[str]     			=						pa.array(
             [encode_np_array(np.array(lowercase_))["bytes"] if arr is not None else None for arr in storage.to_pylist()] ,       type=pa.binary() ,       )
         snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Tuple     			=						pa.StructArray.from_arrays(
             [bytes_array, path_array] ,       ["bytes", "path"] ,       mask=bytes_array.is_null())
     return array_cast(lowercase_ ,       self.pa_type)





 def        snake_case__		(    self ,       lowercase_):

     @no_op_if_value_is_null
     def path_to_bytes(lowercase_):
         with xopen(lowercase_ ,       "rb") as f:
             snake_case_ :    List[Any]     			=						f.read()
         return bytes_

     snake_case_ :    Optional[Any]     			=						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() ,       )
     snake_case_ :    Dict     			=						pa.array(
         [os.path.basename(lowercase_) if path is not None else None for path in storage.field("path").to_pylist()] ,       type=pa.string() ,       )
     snake_case_ :    Tuple     			=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ["bytes", "path"] ,       mask=bytes_array.is_null())
     return array_cast(lowercase_ ,       self.pa_type)


def 						UpperCamelCase_     (	):
    """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()
        snake_case_ :    Dict     			=						list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
    return _IMAGE_COMPRESSION_FORMATS

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						BytesIO()
    if image.format in list_image_compression_formats():
        snake_case_ :    List[Any]     			=						image.format
    else:
        snake_case_ :    Tuple     			=						"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
    image.save(__SCREAMING_SNAKE_CASE,		format=__SCREAMING_SNAKE_CASE       )
    return buffer.getvalue()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    snake_case_ :    str     			=						array.dtype
    snake_case_ :    int     			=						dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
    snake_case_ :    int     			=						dtype.kind
    snake_case_ :    Optional[Any]     			=						dtype.itemsize

    snake_case_ :    Optional[int]     			=						None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        snake_case_ :    List[Any]     			=						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:
        snake_case_ :    List[str]     			=						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:
            snake_case_ :    int     			=						dtype_byteorder + dtype_kind + str(__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Optional[int]     			=						np.dtype(__SCREAMING_SNAKE_CASE       )
            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}'       )
    snake_case_ :    Dict     			=						PIL.Image.fromarray(array.astype(__SCREAMING_SNAKE_CASE       )       )
    return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE       )}


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

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


'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key       )
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case_ :    int     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case_ :    Dict     			=						pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case_ :    List[str]     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case_ :    str     			=						pt_tensor.transpose(2,		3,		1,		0       )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case_ :    str     			=						pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case_ :    Any     			=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        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}.'       )

        # also add unexpected weight so that warning is thrown
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




