






import unittest

import numpy as np
from datasets import load_dataset

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, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					a=[0.5, 0.5, 0.5]		,					a=[0.5, 0.5, 0.5]		,					a=False		,					)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 20, 'width': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= image_mean
						SCREAMING_SNAKE_CASE						= image_std
						SCREAMING_SNAKE_CASE						= do_reduce_labels







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						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_reduce_labels": self.do_reduce_labels,
						}






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= load_dataset('hf-internal-testing/fixtures_ade20k'							,       split='test')

			SCREAMING_SNAKE_CASE						= Image.open(dataset[0]['file'])
			SCREAMING_SNAKE_CASE						= Image.open(dataset[1]['file'])

			return image, map






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= load_dataset('hf-internal-testing/fixtures_ade20k'							,       split='test')

			SCREAMING_SNAKE_CASE						= Image.open(ds[0]['file'])
			SCREAMING_SNAKE_CASE						= Image.open(ds[1]['file'])
			SCREAMING_SNAKE_CASE						= Image.open(ds[2]['file'])
			SCREAMING_SNAKE_CASE						= Image.open(ds[3]['file'])

			return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    List[str]			     =							BeitImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= BeitImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_normalize'))
						self.assertTrue(hasattr(a		,					'image_mean'))
						self.assertTrue(hasattr(a		,					'image_std'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'height': 20, 'width': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})
						self.assertEqual(image_processor.do_reduce_labels		,					a)

						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(
						    self.image_processor_dict		,					size=42		,					crop_size=84		,					reduce_labels=a)
						self.assertEqual(image_processor.size		,					{'height': 42, 'width': 42})
						self.assertEqual(image_processor.crop_size		,					{'height': 84, 'width': 84})
						self.assertEqual(image_processor.do_reduce_labels		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						SCREAMING_SNAKE_CASE						= []
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)
									maps.append(torch.zeros(image.shape[-2:]).long())

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					maps[0]		,					return_tensors='pt')
						self.assertEqual(
						    encoding['pixel_values'].shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(
						    encoding['labels'].shape		,					(
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(encoding['labels'].dtype		,					torch.long)
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 255)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					a		,					return_tensors='pt')
						self.assertEqual(
						    encoding['pixel_values'].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'],
						    )		,					)
						self.assertEqual(
						    encoding['labels'].shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(encoding['labels'].dtype		,					torch.long)
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 255)

						# Test not batched input (PIL images)
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= prepare_semantic_single_inputs()

						SCREAMING_SNAKE_CASE						= image_processing(a		,					a		,					return_tensors='pt')
						self.assertEqual(
						    encoding['pixel_values'].shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(
						    encoding['labels'].shape		,					(
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(encoding['labels'].dtype		,					torch.long)
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 255)

						# Test batched input (PIL images)
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= prepare_semantic_batch_inputs()

						SCREAMING_SNAKE_CASE						= image_processing(a		,					a		,					return_tensors='pt')
						self.assertEqual(
						    encoding['pixel_values'].shape		,					(
						        2,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(
						    encoding['labels'].shape		,					(
						        2,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)
						self.assertEqual(encoding['labels'].dtype		,					torch.long)
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 255)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)

						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= prepare_semantic_single_inputs()
						SCREAMING_SNAKE_CASE						= image_processing(a		,					a		,					return_tensors='pt')
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 150)

						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= image_processing(a		,					a		,					return_tensors='pt')
						self.assertTrue(encoding['labels'].min().item() >= 0)
						self.assertTrue(encoding['labels'].max().item() <= 255)












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# 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
			SCREAMING_SNAKE_CASE						= 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
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 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)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




