




import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
	from tests.trainer.test_trainer import (  # noqa
	    RegressionModelConfig,
	    RegressionPreTrainedModel,
	    get_regression_trainer,
	)


set_seed(4_2)

_lowerCamelCase					:     List[Any]					  =  get_tests_dir("fixtures")
_lowerCamelCase					:     str					  =  os.path.join(dirname(get_tests_dir()))
_lowerCamelCase					:     Any					  =  dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase					:     Tuple					  =  "10999"

_lowerCamelCase					:     str					  =  "t5-small"

# *** Working Models ***
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-albert"
_lowerCamelCase					:     Dict					  =  "sshleifer/bart-tiny-random"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-bert"
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-bigbird_pegasus"
_lowerCamelCase					:     List[Any]					  =  "hf-internal-testing/tiny-random-big_bird"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-random-blenderbot"
_lowerCamelCase					:     Optional[int]					  =  "bigscience/bigscience-small-testing"
_lowerCamelCase					:     List[Any]					  =  "hf-internal-testing/tiny-random-deberta"
_lowerCamelCase					:     Optional[int]					  =  "hf-internal-testing/tiny-random-deberta-v2"
_lowerCamelCase					:     int					  =  "sshleifer/tiny-distilbert-base-cased"
_lowerCamelCase					:     Optional[Any]					  =  "hf-internal-testing/tiny-electra"
_lowerCamelCase					:     List[str]					  =  "hf-internal-testing/tiny-random-flaubert"
_lowerCamelCase					:     Optional[int]					  =  "stas/tiny-wmt19-en-de"
_lowerCamelCase					:     Union[str, Any]					  =  "hf-internal-testing/tiny-random-funnel"
_lowerCamelCase					:     Tuple					  =  "sshleifer/tiny-gpt2"
_lowerCamelCase					:     Optional[Any]					  =  "hf-internal-testing/tiny-random-gptj"
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-gpt_neo"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-layoutlm"
_lowerCamelCase					:     List[Any]					  =  "hf-internal-testing/tiny-random-led"
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-longformer"
_lowerCamelCase					:     int					  =  "stas/tiny-m2m_100"  # hf tiny model is unsuitable
_lowerCamelCase					:     Any					  =  "sshleifer/tiny-marian-en-de"
_lowerCamelCase					:     Dict					  =  "sshleifer/tiny-mbart"
_lowerCamelCase					:     List[Any]					  =  "hf-internal-testing/tiny-random-mobilebert"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-random-mpnet"
_lowerCamelCase					:     List[str]					  =  "stas/pegasus-cnn_dailymail-tiny-random"
_lowerCamelCase					:     List[str]					  =  "hf-internal-testing/tiny-random-prophetnet"
_lowerCamelCase					:     int					  =  "sshleifer/tiny-distilroberta-base"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-squeezebert"
_lowerCamelCase					:     int					  =  "patrickvonplaten/t5-tiny-random"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-random-t5-v1.1"
_lowerCamelCase					:     List[Any]					  =  "hf-internal-testing/tiny-random-vit"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-xlm-roberta"
_lowerCamelCase					:     List[str]					  =  "sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-mt5"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-random-camembert"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-convbert"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-layoutlmv2"
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_lowerCamelCase					:     Optional[Any]					  =  "hf-internal-testing/tiny-random-ctrl"
_lowerCamelCase					:     Optional[int]					  =  "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
_lowerCamelCase					:     str					  =  "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
_lowerCamelCase					:     Union[str, Any]					  =  "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_lowerCamelCase					:     Union[str, Any]					  =  "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
_lowerCamelCase					:     Dict					  =  ""
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase					:     Any					  =  "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase					:     List[str]					  =  "hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase					:     Any					  =  "hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-tapas"




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple=False				):
				'''simple docstring'''
				# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
				# - it won't be able to handle that
				# 2. for now testing with just 2 gpus max (since some quality tests may give different
				# results with mode gpus because we use very little data)
				_lowerCAmelCase			:			Optional[Any]        =					min(2					,			get_gpu_count()				) if distributed else 1
				_lowerCAmelCase			:			Tuple        =					os.environ.get("""DS_TEST_PORT"""					,			UpperCamelCase_				)
				return F"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[Any]        =					F"{FIXTURE_DIRECTORY}/tests_samples"
				_lowerCAmelCase			:			Any        =					F"{data_dir_samples}/wmt_en_ro"
				_lowerCAmelCase			:			Tuple        =					F"{data_dir_samples}/xsum"
				_lowerCAmelCase			:			str        =					"""
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

				# try to cover as many models as possible once (it's enough to run on one task per model)
				# but need a tiny model for each
				#
				# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
				_lowerCAmelCase			:			Optional[Any]        =					{
				    """trans""": [
				        """bart""",
				        """fsmt""",
				        """m2m_100""",
				        """marian""",
				        """mbart""",
				        """t5""",
				        """t5_v1""",
				        # "mt5", missing model files
				    ],
				    """sum""": [
				        """pegasus""",
				    ],
				    """clm""": [
				        """big_bird""",
				        """bigbird_pegasus""",
				        """blenderbot""",
				        """bloom""",
				        """gpt2""",
				        """gpt_neo""",
				        """gptj""",
				        """xlm-roberta""",
				        """prophetnet""",
				        # "camembert", missing model files
				    ],
				    """mlm""": [
				        """albert""",
				        """deberta""",
				        """deberta-v2""",
				        """distilbert""",
				        """electra""",
				        """flaubert""",
				        """funnel""",
				        """layoutlm""",
				        # "reformer", # multiple issues with either mlm/qa/clas
				    ],
				    """qa""": [
				        """led""",
				        """longformer""",
				        """mobilebert""",
				        """mpnet""",
				        """roberta""",
				        """squeezebert""",
				        # "convbert", # missing tokenizer files
				        # "layoutlmv2", missing model files
				    ],
				    """clas""": [
				        """bert""",
				        """xlnet""",
				        # "hubert", # missing tokenizer files
				        # "ibert", # multiple issues with either mlm/qa/clas
				        # "transfo-xl", # tokenizer issues as ctrl
				        # "ctrl", # tokenizer issues
				        # "openai-gpt", missing model files
				        # "tapas", multiple issues
				    ],
				    """img_clas""": [
				        """vit""",
				    ],
				}

				_lowerCAmelCase			:			List[Any]        =					F"{ROOT_DIRECTORY}/examples/pytorch"

				_lowerCAmelCase			:			Dict        =					{
				    """trans""": F"\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ",
				    """sum""": F"\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ",
				    """clm""": F"\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ",
				    """mlm""": F"\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ",
				    """qa""": F"\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ",
				    """clas""": F"\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ",
				    """img_clas""": F"\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ",
				}

				_lowerCAmelCase			:			List[Any]        =					get_launcher(distributed=UpperCamelCase_				)

				_lowerCAmelCase			:			Dict        =					{}
				for task, args in tasks.items():
								_lowerCAmelCase			:			Union[str, Any]        =					args.split()
								for model in tasksamodels[task]:
												_lowerCAmelCase			:			Optional[Any]        =					globals()[F"{model.upper().replace('-'					,			'_'				)}_TINY"]
												_lowerCAmelCase			:			Union[str, Any]        =					F"--model_name_or_path {model_name}".split()
												_lowerCAmelCase			:			Optional[Any]        =					launcher + args + args_model + args_main

												# # generation special case
												# if task == "gen":
												#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
												#     args_model += f"--model_type {model}".split()
												#     cmds[f"{task}_{model}"] = launcher + args + args_model
												# else:

				return cmds


_lowerCamelCase					:     List[str]					  =  make_task_cmds()

_lowerCamelCase					:     str					  =  "zero2"
_lowerCamelCase					:     Union[str, Any]					  =  "zero3"

_lowerCamelCase					:     Union[str, Any]					  =  [ZEROa, ZEROa]




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       str				):
				'''simple docstring'''
				# customize the test name generator function as we want both params to appear in the sub-test
				# name, as by default it shows only the first param
				_lowerCAmelCase			:			int        =					parameterized.to_safe_name("""_""".join(str(UpperCamelCase_				) for x in param.args				)				)
				return F"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
_lowerCamelCase					:     Optional[int]					  =  list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 				__snake_case     (_a						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						str					)     ->			Any:





								'''simple docstring'''




								if task not in task_cmds:
												raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}"					)

								_lowerCAmelCase			:			int        =					task_cmds[task]
								_lowerCAmelCase			:			str        =					f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

								_lowerCAmelCase			:			Optional[int]        =					self.get_auto_remove_tmp_dir()
								_lowerCAmelCase			:			Optional[int]        =					f"--output_dir {output_dir}".split()

								cmd += args_ds + args_out

								return cmd, output_dir



				@parameterized.expand(_UpperCAmelCase						,		name_func=_UpperCAmelCase					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.get_task_cmd(_UpperCAmelCase						,		_UpperCAmelCase					)

								# 1. generate the checkpoint
								cmd += "--save_steps 1".split()
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
								execute_subprocess_async(_UpperCAmelCase						,		env=self.get_env()					)

								# 2. test that the fp32 weights get reconsolidated
								_lowerCAmelCase			:			Dict        =					f"{output_dir}/checkpoint-1"
								_lowerCAmelCase			:			int        =					f"{chkpt_dir}/out.bin"
								_lowerCAmelCase			:			Union[str, Any]        =					f"{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								subprocess.check_call(_UpperCAmelCase						,		shell=_UpperCAmelCase					)
								assert os.path.exists(_UpperCAmelCase					), f"{recovered_model_path} was not found"

								# possibly could also test that the resulting saved model is usable but given that we use
								# random models we won't know if it's any good





'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import CLIPImageProcessor


class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""


    def __init__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[str]=7      ,							SCREAMING_SNAKE_CASE			:							List[Any]=3      ,							SCREAMING_SNAKE_CASE			:							Dict=18      ,							SCREAMING_SNAKE_CASE			:							List[str]=30      ,							SCREAMING_SNAKE_CASE			:							List[Any]=400      ,							SCREAMING_SNAKE_CASE			:							int=True      ,							SCREAMING_SNAKE_CASE			:							Any=None      ,							SCREAMING_SNAKE_CASE			:							str=True      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							List[str]=True      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=[0.4814_5466, 0.457_8275, 0.4082_1073]      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=True      ,							):
           _A     :	List[Any]      =					size if size is not None else {'shortest_edge': 20}
           _A     :	List[Any]      =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
           _A     :	Union[str, Any]      =					parent
           _A     :	Dict      =					batch_size
           _A     :	str      =					num_channels
           _A     :	int      =					image_size
           _A     :	Dict      =					min_resolution
           _A     :	Optional[Any]      =					max_resolution
           _A     :	int      =					do_resize
           _A     :	List[Any]      =					size
           _A     :	Any      =					do_center_crop
           _A     :	List[str]      =					crop_size
           _A     :	List[str]      =					do_normalize
           _A     :	Optional[int]      =					image_mean
           _A     :	Dict      =					image_std
           _A     :	Any      =					do_convert_rgb


    def    A    (  self			:							Tuple):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
           }


    def    A    (  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							str=False      ,							SCREAMING_SNAKE_CASE			:							str=False      ,							SCREAMING_SNAKE_CASE			:							List[Any]=False):
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                  _A     :	Union[str, Any]      =					[]
                  for i in range(self.batch_size):
                         image_inputs.append(
                             np.random.randint(
                                 255      ,							size=(self.num_channels, self.max_resolution, self.max_resolution)      ,							dtype=np.uinta))
           else:
                  _A     :	Union[str, Any]      =					[]
                  for i in range(self.batch_size):
                         _A  , _A     :	Optional[Any]      =					np.random.choice(np.arange(self.min_resolution      ,							self.max_resolution)      ,							2)
                         image_inputs.append(np.random.randint(255      ,							size=(self.num_channels, width, height)      ,							dtype=np.uinta))

           if not numpify and not torchify:
                  # PIL expects the channel dimension as last dimension
                  _A     :	List[str]      =					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE      ,							0      ,							-1)) for x in image_inputs]

           if torchify:
                  _A     :	Optional[Any]      =					[torch.from_numpy(SCREAMING_SNAKE_CASE) for x in image_inputs]

           return image_inputs




@require_torch
@require_vision
class  __lowerCamelCase (       a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    CLIPImageProcessor if is_vision_available() else None


    def    A    (  self			:							Dict):
           _A     :	Tuple      =					CLIPImageProcessingTester(self)


    @property
    def    A    (  self			:							Any):
           return self.image_processor_tester.prepare_image_processor_dict()


    def    A    (  self			:							List[str]):
           _A     :	Optional[int]      =					self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_resize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'size'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_normalize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'image_mean'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'image_std'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_convert_rgb'))


    def    A    (  self			:							Optional[int]):
           _A     :	Optional[Any]      =					self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size      ,							{'shortest_edge': 20})
           self.assertEqual(image_processor.crop_size      ,							{'height': 18, 'width': 18})

           _A     :	Optional[int]      =					self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42      ,							crop_size=84)
           self.assertEqual(image_processor.size      ,							{'shortest_edge': 42})
           self.assertEqual(image_processor.crop_size      ,							{'height': 84, 'width': 84})


    def    A    (  self			:							List[str]):
           pass


    def    A    (  self			:							Optional[int]):
           # Initialize image_processing
           _A     :	Any      =					self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _A     :	List[Any]      =					self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE      ,							Image.Image)

           # Test not batched input
           _A     :	List[str]      =					image_processing(image_inputs[0]      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)

           # Test batched
           _A     :	List[str]      =					image_processing(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)


    def    A    (  self			:							Dict):
           # Initialize image_processing
           _A     :	Optional[int]      =					self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _A     :	Tuple      =					self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE      ,							numpify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE      ,							np.ndarray)

           # Test not batched input
           _A     :	Dict      =					image_processing(image_inputs[0]      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)

           # Test batched
           _A     :	Tuple      =					image_processing(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)


    def    A    (  self			:							str):
           # Initialize image_processing
           _A     :	Optional[Any]      =					self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _A     :	Tuple      =					self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE      ,							torchify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE      ,							torch.Tensor)

           # Test not batched input
           _A     :	Any      =					image_processing(image_inputs[0]      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)

           # Test batched
           _A     :	Union[str, Any]      =					image_processing(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)




@require_torch
@require_vision
class  __lowerCamelCase (       a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    CLIPImageProcessor if is_vision_available() else None


    def    A    (  self			:							int):
           _A     :	Union[str, Any]      =					CLIPImageProcessingTester(self      ,							num_channels=4)
           _A     :	Dict      =					3


    @property
    def    A    (  self			:							Any):
           return self.image_processor_tester.prepare_image_processor_dict()


    def    A    (  self			:							str):
           _A     :	str      =					self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_resize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'size'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'center_crop'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_normalize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'image_mean'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'image_std'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'do_convert_rgb'))


    def    A    (  self			:							str):
           pass


    def    A    (  self			:							int):
           # Initialize image_processing
           _A     :	List[Any]      =					self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _A     :	Union[str, Any]      =					self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE      ,							Image.Image)

           # Test not batched input
           _A     :	int      =					image_processing(image_inputs[0]      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)

           # Test batched
           _A     :	str      =					image_processing(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape      ,							(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )      ,							)






