

"""simple docstring"""




import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

def 	A       (     snake_case__		,				snake_case__=False    ):




			'''simple docstring'''


			try:
						SCREAMING_SNAKE_CASE__								=      os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						SCREAMING_SNAKE_CASE__								=      default
			else:
						# KEY is set, convert it to True or False.
						try:
									SCREAMING_SNAKE_CASE__								=      strtobool(snake_case__    )
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(f"""If set, {key} must be yes or no."""    )
			return _value


A_							:			Dict											= parse_flag_from_env("RUN_SLOW", default=False)
A_							:			str											= parse_flag_from_env("RUN_REMOTE", default=False)
A_							:			Union[str, Any]											= parse_flag_from_env("RUN_LOCAL", default=True)
A_							:			Union[str, Any]											= parse_flag_from_env("RUN_PACKAGED", default=True)

# Compression
A_							:			Optional[Any]											= pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
A_							:			List[Any]											= pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
A_							:			Any											= pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")

# Audio
A_							:			Union[str, Any]											= pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec("soundfile") is None or version.parse(importlib_metadata.version("soundfile")) < version.parse("0.12.0"),
    reason="test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; ",
)

# Beam
A_							:			List[str]											= pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("0.3.2"),
    reason="test requires apache-beam and a compatible dill version",
)

# Dill-cloudpickle compatibility
A_							:			Union[str, Any]											= pytest.mark.skipif(
    config.DILL_VERSION <= version.parse("0.3.2"),
    reason="test requires dill>0.3.2 for cloudpickle compatibility",
)

# Windows
A_							:			Tuple											= pytest.mark.skipif(
    sys.platform == "win32",
    reason="test should not be run on Windows",
)

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import faiss  # noqa
			except ImportError:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires faiss"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import regex  # noqa
			except ImportError:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires regex"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import elasticsearch  # noqa
			except ImportError:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires elasticsearch"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import sqlalchemy  # noqa
			except ImportError:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires sqlalchemy"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not config.TORCH_AVAILABLE:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires PyTorch"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not config.TF_AVAILABLE:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires TensorFlow"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not config.JAX_AVAILABLE:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires JAX"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not config.PIL_AVAILABLE:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires Pillow"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import transformers  # noqa F401
			except ImportError:
						return unittest.skip("""test requires transformers"""    )(snake_case__    )
			else:
						return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import tiktoken  # noqa F401
			except ImportError:
						return unittest.skip("""test requires tiktoken"""    )(snake_case__    )
			else:
						return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import spacy  # noqa F401
			except ImportError:
						return unittest.skip("""test requires spacy"""    )(snake_case__    )
			else:
						return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			def _require_spacy_model(snake_case__    ):
						try:
									import spacy  # noqa F401

									spacy.load(snake_case__    )
						except ImportError:
									return unittest.skip("""test requires spacy"""    )(snake_case__    )
						except OSError:
									return unittest.skip("""test requires spacy model '{}'""".format(snake_case__    )    )(snake_case__    )
						else:
									return test_case

			return _require_spacy_model

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import pyspark  # noqa F401
			except ImportError:
						return unittest.skip("""test requires pyspark"""    )(snake_case__    )
			else:
						return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			try:
						import joblibspark  # noqa F401
			except ImportError:
						return unittest.skip("""test requires joblibspark"""    )(snake_case__    )
			else:
						return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not _run_slow_tests or _run_slow_tests == 0:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test is slow"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not _run_local_tests or _run_local_tests == 0:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test is local"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not _run_packaged_tests or _run_packaged_tests == 0:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test is packaged"""    )(snake_case__    )
			return test_case

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not _run_remote_tests or _run_remote_tests == 0:
						SCREAMING_SNAKE_CASE__								=      unittest.skip("""test requires remote"""    )(snake_case__    )
			return test_case

def 	A       (     *snake_case__    ):




			'''simple docstring'''


			def decorate(cls    ):
						for name, fn in cls.__dict__.items():
									if callable(snake_case__    ) and name.startswith("""test"""    ):
												for decorator in decorators:
															SCREAMING_SNAKE_CASE__								=      decorator(snake_case__    )
												setattr(cls		,				snake_case__		,				snake_case__    )
						return cls

			return decorate





class    lowerCamelCase						(A__			):
			pass





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		str							=							0
			lowerCamelCase__   :		Dict							=							1
			lowerCamelCase__   :		Tuple							=							2

@contextmanager
def 	A       (     snake_case__=OfflineSimulationMode.CONNECTION_FAILS		,				snake_case__=1e-16    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      requests.Session().request

			def timeout_request(snake_case__		,				snake_case__		,				snake_case__		,				**snake_case__    ):
						# Change the url to an invalid url so that the connection hangs
						SCREAMING_SNAKE_CASE__								=      """https://10.255.255.1"""
						if kwargs.get("""timeout"""    ) is None:
									raise RequestWouldHangIndefinitelyError(
									    f"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout."""    )
						SCREAMING_SNAKE_CASE__								=      timeout
						try:
									return online_request(snake_case__		,				snake_case__		,				**snake_case__    )
						except Exception as e:
									# The following changes in the error are just here to make the offline timeout error prettier
									SCREAMING_SNAKE_CASE__								=      url
									SCREAMING_SNAKE_CASE__								=      e.args[0]
									SCREAMING_SNAKE_CASE__								=      (max_retry_error.args[0].replace("""10.255.255.1"""		,				f"""OfflineMock[{url}]"""    ),)
									SCREAMING_SNAKE_CASE__								=      (max_retry_error,)
									raise

			def raise_connection_error(snake_case__		,				snake_case__		,				**snake_case__    ):
						raise requests.ConnectionError("""Offline mode is enabled."""		,				request=snake_case__    )

			if mode is OfflineSimulationMode.CONNECTION_FAILS:
						with patch("""requests.Session.send"""		,				snake_case__    ):
									yield
			elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
						# inspired from https://stackoverflow.com/a/904609
						with patch("""requests.Session.request"""		,				snake_case__    ):
									yield
			elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
						with patch("""datasets.config.HF_DATASETS_OFFLINE"""		,				snake_case__    ):
									yield
			else:
						raise ValueError("""Please use a value from the OfflineSimulationMode enum."""    )

@contextmanager
def 	A       (     *snake_case__		,				**snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      str(Path().resolve()    )
			with tempfile.TemporaryDirectory(*snake_case__		,				**snake_case__    ) as tmp_dir:
						try:
									os.chdir(snake_case__    )
									yield
						finally:
									os.chdir(snake_case__    )

@contextmanager
def 	A       (     ):




			'''simple docstring'''


			import gc

			gc.collect()
			SCREAMING_SNAKE_CASE__								=      pa.total_allocated_bytes()
			yield
			assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."

@contextmanager
def 	A       (     ):




			'''simple docstring'''


			import gc

			gc.collect()
			SCREAMING_SNAKE_CASE__								=      pa.total_allocated_bytes()
			yield
			assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			return deepcopy(snake_case__    ).integers(0		,				1_00		,				10    ).tolist() == deepcopy(snake_case__    ).integers(0		,				1_00		,				10    ).tolist()

def 	A       (     snake_case__    ):




			'''simple docstring'''


			import decorator
			from requests.exceptions import HTTPError

			def _wrapper(snake_case__		,				*snake_case__		,				**snake_case__    ):
						try:
									return func(*snake_case__		,				**snake_case__    )
						except HTTPError as err:
									if str(snake_case__    ).startswith("""500"""    ) or str(snake_case__    ).startswith("""502"""    ):
												pytest.xfail(str(snake_case__    )    )
									raise err

			return decorator.decorator(_wrapper		,				snake_case__    )





class    lowerCamelCase						:

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      returncode
						SCREAMING_SNAKE_CASE__								=      stdout
						SCREAMING_SNAKE_CASE__								=      stderr

async def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			while True:
						SCREAMING_SNAKE_CASE__								=      await stream.readline()
						if line:
									callback(snake_case__    )
						else:
									break

async def 	A       (     snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=False    ):




			'''simple docstring'''


			if echo:
						print("""\nRunning: """		,				""" """.join(snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      await asyncio.create_subprocess_exec(
			    cmd[0]		,				*cmd[1:]		,				stdin=snake_case__		,				stdout=asyncio.subprocess.PIPE		,				stderr=asyncio.subprocess.PIPE		,				env=snake_case__		,				)

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			SCREAMING_SNAKE_CASE__								=      []
			SCREAMING_SNAKE_CASE__								=      []

			def tee(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=""    ):
						SCREAMING_SNAKE_CASE__								=      line.decode("""utf-8"""    ).rstrip()
						sink.append(snake_case__    )
						if not quiet:
									print(snake_case__		,				snake_case__		,				file=snake_case__    )

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        _read_stream(p.stdout		,				lambda snake_case__    : tee(snake_case__		,				snake_case__		,				sys.stdout		,				label="""stdout:"""    )    ),
			        _read_stream(p.stderr		,				lambda snake_case__    : tee(snake_case__		,				snake_case__		,				sys.stderr		,				label="""stderr:"""    )    ),
			    ]		,				timeout=snake_case__		,				)
			return _RunOutput(await p.wait()		,				snake_case__		,				snake_case__    )

def 	A       (     snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=1_80		,				snake_case__=False		,				snake_case__=True    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      asyncio.get_event_loop()
			SCREAMING_SNAKE_CASE__								=      loop.run_until_complete(
			    _stream_subprocess(snake_case__		,				env=snake_case__		,				stdin=snake_case__		,				timeout=snake_case__		,				quiet=snake_case__		,				echo=snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      """ """.join(snake_case__    )
			if result.returncode > 0:
						SCREAMING_SNAKE_CASE__								=      """\n""".join(result.stderr    )
						raise RuntimeError(
						    f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
						    f"""The combined stderr from workers follows:\n{stderr}"""    )

			# check that the subprocess actually did run and produced some output, should the test rely on
			# the remote side to do the testing
			if not result.stdout and not result.stderr:
						raise RuntimeError(f"""'{cmd_str}' produced no output."""    )

			return result

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      os.environ.get("""PYTEST_XDIST_WORKER"""		,				"""gw0"""    )
			SCREAMING_SNAKE_CASE__								=      re.sub(R"""^gw"""		,				""""""		,				snake_case__		,				0		,				re.M    )
			return int(snake_case__    )




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      2_95_00
			SCREAMING_SNAKE_CASE__								=      pytest_xdist_worker_id()
			return port + uniq_delta





"""simple docstring"""




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						)
						from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						Optional[Any]=5    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						int=3_7    ,				__UpperCAmelCase				:						str="gelu"    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=1_0    ,				__UpperCAmelCase				:						Dict=0.02    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						str=2    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      encoder_stride

						# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
						SCREAMING_SNAKE_CASE__								=      (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__								=      num_patches + 2

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE__								=      None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size       )

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						return DeiTConfig(
						    image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				hidden_size=self.hidden_size    ,				num_hidden_layers=self.num_hidden_layers    ,				num_attention_heads=self.num_attention_heads    ,				intermediate_size=self.intermediate_size    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				is_decoder=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      DeiTModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.reconstruction.shape    ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(result.reconstruction.shape    ,				(self.batch_size, 1, self.image_size, self.image_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		
						)								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Tuple							=							(
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		Any							=							(
			    {
			        'feature-extraction': DeiTModel,
			        'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Any							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      DeiTModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									self.assertIsInstance(model.get_input_embeddings()    ,				(nn.Module)       )
									SCREAMING_SNAKE_CASE__								=      model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__UpperCAmelCase    ,				nn.Linear       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=False       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

						if return_labels:
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
												del inputs_dict["labels"]

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						if not self.model_tester.is_training:
									return

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

						for model_class in self.all_model_classes:
									# DeiTForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(__UpperCAmelCase       )
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.train()
									SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
									loss.backward()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__								=      False
						SCREAMING_SNAKE_CASE__								=      True

						for model_class in self.all_model_classes:
									if model_class in get_values(__UpperCAmelCase       ) or not model_class.supports_gradient_checkpointing:
												continue
									# DeiTForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
												continue
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.gradient_checkpointing_enable()
									model.to(__UpperCAmelCase       )
									model.train()
									SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
									loss.backward()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__								=      [
						    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
						    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
						    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
						]

						for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(__UpperCAmelCase       ),
									        *get_values(__UpperCAmelCase       ),
									    ]
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue

									for problem_type in problem_types:
												with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""       ):
															SCREAMING_SNAKE_CASE__								=      problem_type["""title"""]
															SCREAMING_SNAKE_CASE__								=      problem_type["""num_labels"""]

															SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
															model.to(__UpperCAmelCase       )
															model.train()

															SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

															if problem_type["num_labels"] > 1:
																		SCREAMING_SNAKE_CASE__								=      inputs["""labels"""].unsqueeze(1       ).repeat(1    ,				problem_type["""num_labels"""]       )

															SCREAMING_SNAKE_CASE__								=      inputs["""labels"""].to(problem_type["""dtype"""]       )

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=__UpperCAmelCase       ) as warning_list:
																		SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
															for w in warning_list:
																		if "Using a target size that is different to the input size" in str(w.message       ):
																					raise ValueError(
																					    F"""Something is going wrong in the regression problem: intercepted {w.message}"""       )

															loss.backward()





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


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





@require_torch
@require_vision
class    lowerCamelCase						(unittest.TestCase			):

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    List[Any]:
						return (
						    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
						    if is_vision_available()
						    else None
						)

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       ).to(
						    __UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([-1.0_266, 0.1_912, -1.2_861]       ).to(__UpperCAmelCase       )

						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4       )       )





			@slow
			@require_accelerate
			@require_torch_gpu
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(
						    """facebook/deit-base-distilled-patch16-224"""    ,				torch_dtype=torch.floataa    ,				device_map="""auto"""       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor

						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      inputs.pixel_values.to(__UpperCAmelCase       )

						# forward pass to make sure inference works in fp16
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )


