
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	      = None
UpperCAmelCase	      = "<" 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	      = [
    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_   :




	'''simple docstring'''


	_UpperCamelCase     :     List[str]							 =       True
	_UpperCamelCase     :     List[Any]							 =       None
	# Automatically constructed
	_UpperCamelCase     :     Union[str, Any]							 =       """PIL.Image.Image"""
	_UpperCamelCase     :     Dict							 =       pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}   )
	_UpperCamelCase     :     List[Any]							 =       field(default="""Image"""      ,	init=a_      ,	repr=a_   )





	def __call__(       self       ):
		return self.pa_type





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):







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

		if isinstance(_A		,    _A       ):
			lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=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:
			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(_A       ):
					lowercase       =    PIL.Image.open(_A       )
				else:
					lowercase       =    path.split('::'       )[-1]
					try:
						lowercase       =    string_to_dict(_A		,    config.HUB_DATASETS_URL       )['repo_id']
						lowercase       =    token_per_repo_id.get(_A       )
					except ValueError:
						lowercase       =    None
					with xopen(_A		,    'rb'		,    use_auth_token=_A       ) 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 	SCREAMING_SNAKE_CASE__						(       self       ):
		from .features import Value

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):







		if pa.types.is_string(storage.type       ):
			lowercase       =    pa.array([None] * len(_A       )		,    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(_A       )		,    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(_A       )		,    type=pa.binary()       )
			if storage.type.get_field_index('path'       ) >= 0:
				lowercase       =    storage.field('path'       )
			else:
				lowercase       =    pa.array([None] * len(_A       )		,    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(_A       )       )['bytes'] if arr is not None else None for arr in storage.to_pylist()]		,    type=pa.binary()		,    )
			lowercase       =    pa.array([None] * len(_A       )		,    type=pa.string()       )
			lowercase       =    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__						(       self		,    snake_case       ):







		@no_op_if_value_is_null
		def path_to_bytes(snake_case       ):
			with xopen(_A		,    '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(_A       ) 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(_A		,    self.pa_type       )






def 							UpperCAmelCase_		(							):

	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_		(							__SCREAMING_SNAKE_CASE							):
	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(UpperCamelCase__ ,							format=UpperCamelCase__							)
	return buffer.getvalue()






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	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(UpperCamelCase__							)
			lowercase       =    np.dtype(UpperCamelCase__							)
			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(UpperCamelCase__							)							)
	return {"path": None, "bytes": image_to_bytes(UpperCamelCase__							)}






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

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








import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



