

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


UpperCAmelCase_							:					Optional[List[str]]		 =   None
UpperCAmelCase_							:					Union[str, Any]		 =   '''<''' 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
UpperCAmelCase_							:					List[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      lowerCamelCase_      :
	_lowercase		:   bool     					=				True
	_lowercase		:   Optional[str]     					=				None
	# Automatically constructed
	_lowercase		:   ClassVar[str]     					=				"PIL.Image.Image"
	_lowercase		:   ClassVar[Any]     					=				pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}	)
	_lowercase		:   str     					=				field(default='''Image'''						,      init=_lowercase						,      repr=_lowercase	)




	def __call__(     self						:     Tuple							):
		return self.pa_type




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]							):
		if config.PIL_AVAILABLE:
			import PIL.Image
		else:
			raise ImportError("""To support encoding images, please install 'Pillow'."""							)

		if isinstance(__A					,      __A							):
			__A :			Optional[Any]		    =			np.array(__A							)

		if isinstance(__A					,      __A							):
			return {"path": value, "bytes": None}
		elif isinstance(__A					,      __A							):
			return {"path": None, "bytes": value}
		elif isinstance(__A					,      np.ndarray							):
			# convert the image array to PNG/TIFF bytes
			return encode_np_array(__A							)
		elif isinstance(__A					,      PIL.Image.Image							):
			# convert the PIL image to bytes (default format is PNG/TIFF)
			return encode_pil_image(__A							)
		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        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     dict					,      __A						:     Any=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:
			__A :			int		    =			{}

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




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		from .features import Value

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




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




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     pa.StructArray							):

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

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






def 							__SCREAMING_SNAKE_CASE     (   )					->				List[str]:
	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()
		__A :			Dict		    =			list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
	return _IMAGE_COMPRESSION_FORMATS
def 							__SCREAMING_SNAKE_CASE     (   a__    :   "PIL.Image.Image"       )					->				bytes:
	__A :			Dict		    =			BytesIO()
	if image.format in list_image_compression_formats():
		__A :			int		    =			image.format
	else:
		__A :			Any		    =			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
	image.save(a__			,format=a__       )
	return buffer.getvalue()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   "PIL.Image.Image"       )					->				dict:
	if hasattr(a__			,"""filename"""       ) and image.filename != "":
		return {"path": image.filename, "bytes": None}
	else:
		return {"path": None, "bytes": image_to_bytes(a__       )}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   np.ndarray       )					->				dict:
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("""To support encoding images, please install 'Pillow'."""       )

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

	__A :			str		    =			None

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







def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]       )					->				List[dict]:
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError("""To support encoding images, please install 'Pillow'."""       )

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








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





