

import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__UpperCAmelCase    				=     sys.version_info >= (3, 1_0)







def 	_snake_case		(    SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None				)       ->      Union[str, Any]:

     """simple docstring"""







     return field(default_factory=lambda: default		,							metadata=SCREAMING_SNAKE_CASE				)





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					int
 _lowercase     :					float
 _lowercase     :					str
 _lowercase     :					bool





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					int      =						42
 _lowercase     :					str      =						field(default='''toto'''  ,		metadata={'''help''': '''help message'''} )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					bool      =						False
 _lowercase     :					bool      =						True
 _lowercase     :					Optional[bool]      =						None





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Tuple      =						'''titi'''
 _lowercase     :					Union[str, Any]      =						'''toto'''





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Tuple      =						'''titi'''
 _lowercase     :					List[Any]      =						'''toto'''
 _lowercase     :					Optional[int]      =						42





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					BasicEnum      =						"toto"

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    BasicEnum(self.foo       )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					MixedTypeEnum      =						"toto"

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    MixedTypeEnum(self.foo       )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					Optional[int]      =						None
 _lowercase     :					Optional[float]      =						field(default=A  ,		metadata={'''help''': '''help message'''} )
 _lowercase     :					Optional[str]      =						None
 _lowercase     :					Optional[List[str]]      =						list_field(default=[] )
 _lowercase     :					Optional[List[int]]      =						list_field(default=[] )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					List[int]      =						list_field(default=[] )
 _lowercase     :					List[int]      =						list_field(default=[1, 2, 3] )
 _lowercase     :					List[str]      =						list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
 _lowercase     :					List[float]      =						list_field(default=[0.1, 0.2, 0.3] )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					List[int]      =						field()
 _lowercase     :					str      =						field()
 _lowercase     :					BasicEnum      =						field()

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    BasicEnum(self.required_enum       )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					int
 _lowercase     :					"BasicEnum"      =						field()
 _lowercase     :					"Optional[bool]"      =						None
 _lowercase     :					"str"      =						field(default='''toto'''  ,		metadata={'''help''': '''help message'''} )
 _lowercase     :					"List[str]"      =						list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )


if is_python_no_less_than_3_10:





  @dataclass
  class A__    :



   """simple docstring"""







   _lowercase     :					bool      =						False
   _lowercase     :					bool      =						True
   _lowercase     :					bool | None      =						None





  @dataclass
  class A__    :



   """simple docstring"""







   _lowercase     :					int | None      =						None
   _lowercase     :					float | None      =						field(default=A  ,		metadata={'''help''': '''help message'''} )
   _lowercase     :					str | None      =						None
   _lowercase     :					list[str] | None      =						list_field(default=[] )
   _lowercase     :					list[int] | None      =						list_field(default=[] )





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	argparse.ArgumentParser     ,						A_ :	argparse.ArgumentParser       ):






      '''simple docstring'''




      self.assertEqual(len(a._actions       )     ,						len(b._actions       )       )
      for x, y in zip(a._actions     ,						b._actions       ):
           _lowerCAmelCase   :					Optional[int]        =    {k: v for k, v in vars(A_       ).items() if k != "container"}
           _lowerCAmelCase   :					Tuple        =    {k: v for k, v in vars(A_       ).items() if k != "container"}

           # Choices with mixed type have custom function as "type"
           # So we need to compare results directly for equality
           if xx.get("choices"     ,						A_       ) and yy.get("choices"     ,						A_       ):
                for expected_choice in yy["choices"] + xx["choices"]:
                     self.assertEqual(xx["type"](A_       )     ,						yy["type"](A_       )       )
                del xx["type"], yy["type"]

           self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					List[Any]        =    argparse.ArgumentParser()
      expected.add_argument("--foo"     ,						type=A_     ,						required=A_       )
      expected.add_argument("--bar"     ,						type=A_     ,						required=A_       )
      expected.add_argument("--baz"     ,						type=A_     ,						required=A_       )
      expected.add_argument("--flag"     ,						type=A_     ,						default=A_     ,						const=A_     ,						nargs="?"       )
      self.argparsersEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Dict        =    ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
      ((_lowerCAmelCase)      ,			)   :					Union[str, Any]        =    parser.parse_args_into_dataclasses(A_     ,						look_for_args_file=A_       )
      self.assertFalse(example.flag       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					List[str]        =    argparse.ArgumentParser()
      expected.add_argument("--foo"     ,						default=4_2     ,						type=A_       )
      expected.add_argument("--baz"     ,						default="toto"     ,						type=A_     ,						help="help message"       )
      self.argparsersEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    argparse.ArgumentParser()
      expected.add_argument("--foo"     ,						type=A_     ,						default=A_     ,						const=A_     ,						nargs="?"       )
      expected.add_argument("--baz"     ,						type=A_     ,						default=A_     ,						const=A_     ,						nargs="?"       )
      # A boolean no_* argument always has to come after its "default: True" regular counter-part
      # and its default must be set to False
      expected.add_argument("--no_baz"     ,						action="store_false"     ,						default=A_     ,						dest="baz"       )
      expected.add_argument("--opt"     ,						type=A_     ,						default=A_       )

      _lowerCAmelCase   :					str        =    [WithDefaultBoolExample]
      if is_python_no_less_than_3_10:
           dataclass_types.append(A_       )

      for dataclass_type in dataclass_types:
           _lowerCAmelCase   :					Dict        =    HfArgumentParser(A_       )
           self.argparsersEqual(A_     ,						A_       )

           _lowerCAmelCase   :					Optional[int]        =    parser.parse_args([]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						baz=A_     ,						opt=A_       )       )

           _lowerCAmelCase   :					str        =    parser.parse_args(["--foo", "--no_baz"]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						baz=A_     ,						opt=A_       )       )

           _lowerCAmelCase   :					Any        =    parser.parse_args(["--foo", "--baz"]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						baz=A_     ,						opt=A_       )       )

           _lowerCAmelCase   :					Optional[Any]        =    parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						baz=A_     ,						opt=A_       )       )

           _lowerCAmelCase   :					List[Any]        =    parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						baz=A_     ,						opt=A_       )       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					List[str]        =    argparse.ArgumentParser()
      expected.add_argument(
          "--foo"     ,						default="toto"     ,						choices=["titi", "toto", 4_2]     ,						type=make_choice_type_function(["titi", "toto", 4_2]       )     ,						)
      self.argparsersEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Dict        =    parser.parse_args([]       )
      self.assertEqual(args.foo     ,						"toto"       )
      _lowerCAmelCase   :					Any        =    parser.parse_args_into_dataclasses([]       )[0]
      self.assertEqual(enum_ex.foo     ,						MixedTypeEnum.toto       )

      _lowerCAmelCase   :					List[Any]        =    parser.parse_args(["--foo", "titi"]       )
      self.assertEqual(args.foo     ,						"titi"       )
      _lowerCAmelCase   :					Optional[Any]        =    parser.parse_args_into_dataclasses(["--foo", "titi"]       )[0]
      self.assertEqual(enum_ex.foo     ,						MixedTypeEnum.titi       )

      _lowerCAmelCase   :					Optional[Any]        =    parser.parse_args(["--foo", "42"]       )
      self.assertEqual(args.foo     ,						4_2       )
      _lowerCAmelCase   :					Optional[Any]        =    parser.parse_args_into_dataclasses(["--foo", "42"]       )[0]
      self.assertEqual(enum_ex.foo     ,						MixedTypeEnum.fourtytwo       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      @dataclass
      class A__    :



       """simple docstring"""







       _lowercase     :					Literal["titi", "toto", 42]      =						"toto"

      _lowerCAmelCase   :					int        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					int        =    argparse.ArgumentParser()
      expected.add_argument(
          "--foo"     ,						default="toto"     ,						choices=("titi", "toto", 4_2)     ,						type=make_choice_type_function(["titi", "toto", 4_2]       )     ,						)
      self.argparsersEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    parser.parse_args([]       )
      self.assertEqual(args.foo     ,						"toto"       )

      _lowerCAmelCase   :					Tuple        =    parser.parse_args(["--foo", "titi"]       )
      self.assertEqual(args.foo     ,						"titi"       )

      _lowerCAmelCase   :					str        =    parser.parse_args(["--foo", "42"]       )
      self.assertEqual(args.foo     ,						4_2       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Optional[Any]        =    argparse.ArgumentParser()
      expected.add_argument("--foo_int"     ,						nargs="+"     ,						default=[]     ,						type=A_       )
      expected.add_argument("--bar_int"     ,						nargs="+"     ,						default=[1, 2, 3]     ,						type=A_       )
      expected.add_argument("--foo_str"     ,						nargs="+"     ,						default=["Hallo", "Bonjour", "Hello"]     ,						type=A_       )
      expected.add_argument("--foo_float"     ,						nargs="+"     ,						default=[0.1, 0.2, 0.3]     ,						type=A_       )

      self.argparsersEqual(A_     ,						A_       )

      _lowerCAmelCase   :					str        =    parser.parse_args([]       )
      self.assertEqual(
          A_     ,						Namespace(foo_int=[]     ,						bar_int=[1, 2, 3]     ,						foo_str=["Hallo", "Bonjour", "Hello"]     ,						foo_float=[0.1, 0.2, 0.3]       )     ,						)

      _lowerCAmelCase   :					List[str]        =    parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split()       )
      self.assertEqual(A_     ,						Namespace(foo_int=[1]     ,						bar_int=[2, 3]     ,						foo_str=["a", "b", "c"]     ,						foo_float=[0.1, 0.7]       )       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    argparse.ArgumentParser()
      expected.add_argument("--foo"     ,						default=A_     ,						type=A_       )
      expected.add_argument("--bar"     ,						default=A_     ,						type=A_     ,						help="help message"       )
      expected.add_argument("--baz"     ,						default=A_     ,						type=A_       )
      expected.add_argument("--ces"     ,						nargs="+"     ,						default=[]     ,						type=A_       )
      expected.add_argument("--des"     ,						nargs="+"     ,						default=[]     ,						type=A_       )

      _lowerCAmelCase   :					Any        =    [OptionalExample]
      if is_python_no_less_than_3_10:
           dataclass_types.append(A_       )

      for dataclass_type in dataclass_types:
           _lowerCAmelCase   :					Tuple        =    HfArgumentParser(A_       )

           self.argparsersEqual(A_     ,						A_       )

           _lowerCAmelCase   :					str        =    parser.parse_args([]       )
           self.assertEqual(A_     ,						Namespace(foo=A_     ,						bar=A_     ,						baz=A_     ,						ces=[]     ,						des=[]       )       )

           _lowerCAmelCase   :					Union[str, Any]        =    parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split()       )
           self.assertEqual(A_     ,						Namespace(foo=1_2     ,						bar=3.14     ,						baz="42"     ,						ces=["a", "b", "c"]     ,						des=[1, 2, 3]       )       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Optional[int]        =    argparse.ArgumentParser()
      expected.add_argument("--required_list"     ,						nargs="+"     ,						type=A_     ,						required=A_       )
      expected.add_argument("--required_str"     ,						type=A_     ,						required=A_       )
      expected.add_argument(
          "--required_enum"     ,						type=make_choice_type_function(["titi", "toto"]       )     ,						choices=["titi", "toto"]     ,						required=A_     ,						)
      self.argparsersEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Tuple        =    argparse.ArgumentParser()
      expected.add_argument("--foo"     ,						type=A_     ,						required=A_       )
      expected.add_argument(
          "--required_enum"     ,						type=make_choice_type_function(["titi", "toto"]       )     ,						choices=["titi", "toto"]     ,						required=A_     ,						)
      expected.add_argument("--opt"     ,						type=A_     ,						default=A_       )
      expected.add_argument("--baz"     ,						default="toto"     ,						type=A_     ,						help="help message"       )
      expected.add_argument("--foo_str"     ,						nargs="+"     ,						default=["Hallo", "Bonjour", "Hello"]     ,						type=A_       )
      self.argparsersEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Any        =    {
          "foo": 1_2,
          "bar": 3.14,
          "baz": "42",
          "flag": True,
      }

      _lowerCAmelCase   :					List[Any]        =    parser.parse_dict(A_       )[0]
      _lowerCAmelCase   :					List[Any]        =    BasicExample(**A_       )
      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Any        =    {
          "foo": 1_2,
          "bar": 3.14,
          "baz": "42",
          "flag": True,
          "extra": 4_2,
      }

      self.assertRaises(A_     ,						parser.parse_dict     ,						A_     ,						allow_extra_keys=A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    {
          "foo": 1_2,
          "bar": 3.14,
          "baz": "42",
          "flag": True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
           _lowerCAmelCase   :					Tuple        =    os.path.join(A_     ,						"temp_json"       )
           os.mkdir(A_       )
           with open(temp_local_path + ".json"     ,						"w+"       ) as f:
                json.dump(A_     ,						A_       )
           _lowerCAmelCase   :					List[str]        =    parser.parse_yaml_file(Path(temp_local_path + ".json"       )       )[0]

      _lowerCAmelCase   :					str        =    BasicExample(**A_       )
      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    HfArgumentParser(A_       )

      _lowerCAmelCase   :					List[str]        =    {
          "foo": 1_2,
          "bar": 3.14,
          "baz": "42",
          "flag": True,
      }
      with tempfile.TemporaryDirectory() as tmp_dir:
           _lowerCAmelCase   :					Optional[int]        =    os.path.join(A_     ,						"temp_yaml"       )
           os.mkdir(A_       )
           with open(temp_local_path + ".yaml"     ,						"w+"       ) as f:
                yaml.dump(A_     ,						A_       )
           _lowerCAmelCase   :					List[Any]        =    parser.parse_yaml_file(Path(temp_local_path + ".yaml"       )       )[0]
      _lowerCAmelCase   :					Optional[int]        =    BasicExample(**A_       )
      self.assertEqual(A_     ,						A_       )


 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    HfArgumentParser(A_       )
      self.assertIsNotNone(A_       )









from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						['''pixel_values''']

 def __init__(		self :	Union[str, Any]     ,						A_ :	bool = True     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	Optional[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else {"shortest_edge": 2_5_6}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Dict        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Tuple        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Dict        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					Union[str, Any]        =    resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop
      _lowerCAmelCase   :					str        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					str        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Optional[Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	int     ,						A_ :	np.ndarray     ,						A_ :	float     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[str]     ,						A_ :	ImageInput     ,						A_ :	Optional[bool] = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[float] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	Union[str, ChannelDimension] = ChannelDimension.FIRST     ,						**A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else self.size
      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Union[str, Any]        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[str]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					Optional[Any]        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					Any        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					str        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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   :					Any        =    [to_numpy_array(A_       ) for image in images]

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

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

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

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

      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

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


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(A_       ) != len(A_       ):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					List[str]        =    target_sizes.numpy()

           _lowerCAmelCase   :					Tuple        =    []

           for idx in range(len(A_       )       ):
                _lowerCAmelCase   :					Tuple        =    torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0       )     ,						size=target_sizes[idx]     ,						mode="bilinear"     ,						align_corners=A_       )
                _lowerCAmelCase   :					str        =    resized_logits[0].argmax(dim=0       )
                semantic_segmentation.append(A_       )
      else:
           _lowerCAmelCase   :					List[Any]        =    logits.argmax(dim=1       )
           _lowerCAmelCase   :					str        =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation






