






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
a_							=    sys.version_info >= (3, 10)


def        _a  (     UpperCamelCase_					:				Dict=None    ,   UpperCamelCase_					:				Any=None   ) ->   str:
						"""simple docstring"""







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


@dataclass
class      lowercase__     :
	a_         =42
	a_         =42
	a_         =42
	a_         =42





@dataclass
class      lowercase__     :
	a_         =42
	a_         =field(default="""toto""",					metadata={"""help""": """help message"""}	)





@dataclass
class      lowercase__     :
	a_         =False
	a_         =True
	a_         =None





class      lowercase__     (			_UpperCAmelCase	):
	a_         ="""titi"""
	a_         ="""toto"""





class      lowercase__     (			_UpperCAmelCase	):
	a_         ="""titi"""
	a_         ="""toto"""
	a_         =42





@dataclass
class      lowercase__     :
	a_         ="toto"

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						BasicEnum(self.foo       )





@dataclass
class      lowercase__     :
	a_         ="toto"

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						MixedTypeEnum(self.foo       )





@dataclass
class      lowercase__     :
	a_         =None
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """help message"""}	)
	a_         =None
	a_         =list_field(default=[]	)
	a_         =list_field(default=[]	)





@dataclass
class      lowercase__     :
	a_         =list_field(default=[]	)
	a_         =list_field(default=[1, 2, 3]	)
	a_         =list_field(default=["""Hallo""", """Bonjour""", """Hello"""]	)
	a_         =list_field(default=[0.1, 0.2, 0.3]	)





@dataclass
class      lowercase__     :
	a_         =field()
	a_         =field()
	a_         =field()

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						BasicEnum(self.required_enum       )





@dataclass
class      lowercase__     :
	a_         =42
	a_         =field()
	a_         =None
	a_         =field(default="""toto""",					metadata={"""help""": """help message"""}	)
	a_         =list_field(default=["""Hallo""", """Bonjour""", """Hello"""]	)


if is_python_no_less_than_3_10:


	@dataclass
	class      lowercase__     :
		a_         =False
		a_         =True
		a_         =None





	@dataclass
	class      lowercase__     :
		a_         =None
		a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """help message"""}	)
		a_         =None
		a_         =list_field(default=[]	)
		a_         =list_field(default=[]	)





class      lowercase__     (			unittest.TestCase	):

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							self.assertEqual(len(a._actions       )		,    len(b._actions       )       )
							for x, y in zip(a._actions		,    b._actions       ):
													lowerCAmelCase__       =						{k: v for k, v in vars(__UpperCAmelCase       ).items() if k != "container"}
													lowerCAmelCase__       =						{k: v for k, v in vars(__UpperCAmelCase       ).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"		,    __UpperCAmelCase       ) and yy.get("choices"		,    __UpperCAmelCase       ):
																			for expected_choice in yy["choices"] + xx["choices"]:
																									self.assertEqual(xx["type"](__UpperCAmelCase       )		,    yy["type"](__UpperCAmelCase       )       )
																			del xx["type"], yy["type"]

													self.assertEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument("--foo"		,    type=__UpperCAmelCase		,    required=__UpperCAmelCase       )
							expected.add_argument("--bar"		,    type=__UpperCAmelCase		,    required=__UpperCAmelCase       )
							expected.add_argument("--baz"		,    type=__UpperCAmelCase		,    required=__UpperCAmelCase       )
							expected.add_argument("--flag"		,    type=__UpperCAmelCase		,    default=__UpperCAmelCase		,    const=__UpperCAmelCase		,    nargs="?"       )
							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						["--foo", "1", "--baz", "quux", "--bar", "0.5"]
							((lowerCAmelCase__)      ,							)       =						parser.parse_args_into_dataclasses(__UpperCAmelCase		,    look_for_args_file=__UpperCAmelCase       )
							self.assertFalse(example.flag       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument("--foo"		,    default=42		,    type=__UpperCAmelCase       )
							expected.add_argument("--baz"		,    default="toto"		,    type=__UpperCAmelCase		,    help="help message"       )
							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument("--foo"		,    type=__UpperCAmelCase		,    default=__UpperCAmelCase		,    const=__UpperCAmelCase		,    nargs="?"       )
							expected.add_argument("--baz"		,    type=__UpperCAmelCase		,    default=__UpperCAmelCase		,    const=__UpperCAmelCase		,    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=__UpperCAmelCase		,    dest="baz"       )
							expected.add_argument("--opt"		,    type=__UpperCAmelCase		,    default=__UpperCAmelCase       )

							lowerCAmelCase__       =						[WithDefaultBoolExample]
							if is_python_no_less_than_3_10:
													dataclass_types.append(__UpperCAmelCase       )

							for dataclass_type in dataclass_types:
													lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )
													self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						parser.parse_args([]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    opt=__UpperCAmelCase       )       )

													lowerCAmelCase__       =						parser.parse_args(["--foo", "--no_baz"]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    opt=__UpperCAmelCase       )       )

													lowerCAmelCase__       =						parser.parse_args(["--foo", "--baz"]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    opt=__UpperCAmelCase       )       )

													lowerCAmelCase__       =						parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    opt=__UpperCAmelCase       )       )

													lowerCAmelCase__       =						parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    opt=__UpperCAmelCase       )       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument(
							    "--foo"		,    default="toto"		,    choices=["titi", "toto", 42]		,    type=make_choice_type_function(["titi", "toto", 42]       )		,    )
							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						parser.parse_args([]       )
							self.assertEqual(args.foo		,    "toto"       )
							lowerCAmelCase__       =						parser.parse_args_into_dataclasses([]       )[0]
							self.assertEqual(enum_ex.foo		,    MixedTypeEnum.toto       )

							lowerCAmelCase__       =						parser.parse_args(["--foo", "titi"]       )
							self.assertEqual(args.foo		,    "titi"       )
							lowerCAmelCase__       =						parser.parse_args_into_dataclasses(["--foo", "titi"]       )[0]
							self.assertEqual(enum_ex.foo		,    MixedTypeEnum.titi       )

							lowerCAmelCase__       =						parser.parse_args(["--foo", "42"]       )
							self.assertEqual(args.foo		,    42       )
							lowerCAmelCase__       =						parser.parse_args_into_dataclasses(["--foo", "42"]       )[0]
							self.assertEqual(enum_ex.foo		,    MixedTypeEnum.fourtytwo       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							@dataclass
							class      lowercase__     :
								a_         ="toto"





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument(
							    "--foo"		,    default="toto"		,    choices=("titi", "toto", 42)		,    type=make_choice_type_function(["titi", "toto", 42]       )		,    )
							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						parser.parse_args([]       )
							self.assertEqual(args.foo		,    "toto"       )

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

							lowerCAmelCase__       =						parser.parse_args(["--foo", "42"]       )
							self.assertEqual(args.foo		,    42       )

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

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

							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

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

							lowerCAmelCase__       =						parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split()       )
							self.assertEqual(__UpperCAmelCase		,    Namespace(foo_int=[1]		,    bar_int=[2, 3]		,    foo_str=["a", "b", "c"]		,    foo_float=[0.1, 0.7]       )       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument("--foo"		,    default=__UpperCAmelCase		,    type=__UpperCAmelCase       )
							expected.add_argument("--bar"		,    default=__UpperCAmelCase		,    type=__UpperCAmelCase		,    help="help message"       )
							expected.add_argument("--baz"		,    default=__UpperCAmelCase		,    type=__UpperCAmelCase       )
							expected.add_argument("--ces"		,    nargs="+"		,    default=[]		,    type=__UpperCAmelCase       )
							expected.add_argument("--des"		,    nargs="+"		,    default=[]		,    type=__UpperCAmelCase       )

							lowerCAmelCase__       =						[OptionalExample]
							if is_python_no_less_than_3_10:
													dataclass_types.append(__UpperCAmelCase       )

							for dataclass_type in dataclass_types:
													lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

													self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						parser.parse_args([]       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=__UpperCAmelCase		,    bar=__UpperCAmelCase		,    baz=__UpperCAmelCase		,    ces=[]		,    des=[]       )       )

													lowerCAmelCase__       =						parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split()       )
													self.assertEqual(__UpperCAmelCase		,    Namespace(foo=12		,    bar=3.14		,    baz="42"		,    ces=["a", "b", "c"]		,    des=[1, 2, 3]       )       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						argparse.ArgumentParser()
							expected.add_argument("--required_list"		,    nargs="+"		,    type=__UpperCAmelCase		,    required=__UpperCAmelCase       )
							expected.add_argument("--required_str"		,    type=__UpperCAmelCase		,    required=__UpperCAmelCase       )
							expected.add_argument(
							    "--required_enum"		,    type=make_choice_type_function(["titi", "toto"]       )		,    choices=["titi", "toto"]		,    required=__UpperCAmelCase		,    )
							self.argparsersEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

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

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						{
							    "foo": 12,
							    "bar": 3.14,
							    "baz": "42",
							    "flag": True,
							}

							lowerCAmelCase__       =						parser.parse_dict(__UpperCAmelCase       )[0]
							lowerCAmelCase__       =						BasicExample(**__UpperCAmelCase       )
							self.assertEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						{
							    "foo": 12,
							    "bar": 3.14,
							    "baz": "42",
							    "flag": True,
							    "extra": 42,
							}

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

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						{
							    "foo": 12,
							    "bar": 3.14,
							    "baz": "42",
							    "flag": True,
							}
							with tempfile.TemporaryDirectory() as tmp_dir:
													lowerCAmelCase__       =						os.path.join(__UpperCAmelCase		,    "temp_json"       )
													os.mkdir(__UpperCAmelCase       )
													with open(temp_local_path + ".json"		,    "w+"       ) as f:
																			json.dump(__UpperCAmelCase		,    __UpperCAmelCase       )
													lowerCAmelCase__       =						parser.parse_yaml_file(Path(temp_local_path + ".json"       )       )[0]

							lowerCAmelCase__       =						BasicExample(**__UpperCAmelCase       )
							self.assertEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )

							lowerCAmelCase__       =						{
							    "foo": 12,
							    "bar": 3.14,
							    "baz": "42",
							    "flag": True,
							}
							with tempfile.TemporaryDirectory() as tmp_dir:
													lowerCAmelCase__       =						os.path.join(__UpperCAmelCase		,    "temp_yaml"       )
													os.mkdir(__UpperCAmelCase       )
													with open(temp_local_path + ".yaml"		,    "w+"       ) as f:
																			yaml.dump(__UpperCAmelCase		,    __UpperCAmelCase       )
													lowerCAmelCase__       =						parser.parse_yaml_file(Path(temp_local_path + ".yaml"       )       )[0]
							lowerCAmelCase__       =						BasicExample(**__UpperCAmelCase       )
							self.assertEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						HfArgumentParser(__UpperCAmelCase       )
							self.assertIsNotNone(__UpperCAmelCase       )














import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor


class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "hidden_sizes"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_attention_heads"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_encoder_blocks"       )       )





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=64		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=[2, 2, 2, 2]		,    __UpperCAmelCase=[8, 4, 2, 1]		,    __UpperCAmelCase=[16, 32, 64, 128]		,    __UpperCAmelCase=[1, 4, 8, 16]		,    __UpperCAmelCase=[1, 2, 4, 8]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						num_encoder_blocks
							lowerCAmelCase__       =						sr_ratios
							lowerCAmelCase__       =						depths
							lowerCAmelCase__       =						hidden_sizes
							lowerCAmelCase__       =						downsampling_rates
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						hidden_act
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						scope

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							lowerCAmelCase__       =						None
							if self.use_labels:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels       )

							lowerCAmelCase__       =						self.get_config()
							return config, pixel_values, labels

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							return SegformerConfig(
							    image_size=self.image_size		,    num_channels=self.num_channels		,    num_encoder_blocks=self.num_encoder_blocks		,    depths=self.depths		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							lowerCAmelCase__       =						lowerCAmelCase__       =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							self.parent.assertGreater(result.loss		,    0.0       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						torch.randint(0		,    1		,    (self.batch_size, self.image_size, self.image_size)       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertGreater(result.loss		,    0.0       )


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						config_and_inputs
							lowerCAmelCase__       =						{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": SegformerModel,
	        """image-classification""": SegformerForImageClassification,
	        """image-segmentation""": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a_         =True
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModelTester(self       )
							lowerCAmelCase__       =						SegformerConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase       )

	@unittest.skip("SegFormer does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													lowerCAmelCase__       =						inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCAmelCase__       =						[*signature.parameters.keys()]

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						False
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													lowerCAmelCase__       =						sum(self.model_tester.depths       )
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

													# verify the last attentions (last block, last layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 32) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]       )		,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,    )
													lowerCAmelCase__       =						len(__UpperCAmelCase       )

													# Check attention is always last and order is fine
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													self.assertEqual(out_len + 1		,    len(__UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )
													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							def check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()

													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.hidden_states

													lowerCAmelCase__       =						self.model_tester.num_encoder_blocks
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]       )		,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]		,    )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCAmelCase__       =						True

													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							if not self.model_tester.is_training:
													return

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													if model_class in get_values(__UpperCAmelCase       ):
																			continue

													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.train()
													lowerCAmelCase__       =						self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase		,    return_labels=__UpperCAmelCase       )
													lowerCAmelCase__       =						model(**__UpperCAmelCase       ).loss
													loss.backward()

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						SegformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





def        _a  (     ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
							        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
							        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

	@slow
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained(
							    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
							        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
							        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-1       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						outputs.logits.detach().cpu()

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		,    target_sizes=[(500, 300)]       )
							lowerCAmelCase__       =						torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.Size((128, 128)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )






