



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      Optional[int]		,   _A      :      Tuple		,   _A      :      Optional[int]=7		,   _A      :      List[str]=3		,   _A      :      str=30		,   _A      :      Tuple=400		,   _A      :      int=True		,   _A      :      int=None		,   _A      :      int=True		,   _A      :      int=[0.5, 0.5, 0.5]		,   _A      :      Dict=[0.5, 0.5, 0.5]		,   _A      :      Any=True		,   _A      :      Any=1 / 255		,   _A      :      List[Any]=True		,   )							->    int:







							"""simple docstring"""


							snake_case_		:			int											=	size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							snake_case_		:			Any											=	parent
							snake_case_		:			Any											=	batch_size
							snake_case_		:			str											=	num_channels
							snake_case_		:			Any											=	min_resolution
							snake_case_		:			int											=	max_resolution
							snake_case_		:			List[str]											=	do_resize
							snake_case_		:			List[Any]											=	size
							snake_case_		:			List[Any]											=	do_normalize
							snake_case_		:			Any											=	image_mean
							snake_case_		:			Dict											=	image_std
							snake_case_		:			List[str]											=	do_rescale
							snake_case_		:			Any											=	rescale_factor
							snake_case_		:			Optional[int]											=	do_pad






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}






						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Tuple=False	)							->    Any:







							"""simple docstring"""


							if not batched:
								snake_case_		:			Tuple											=	image_inputs[0]
								if isinstance(_a		,   Image.Image	):
									snake_case_						,snake_case_		:			int											=	image.size
								else:
									snake_case_						,snake_case_		:			str											=	image.shape[1], image.shape[2]
								if w < h:
									snake_case_		:			str											=	int(self.size['shortest_edge'] * h / w	)
									snake_case_		:			List[Any]											=	self.size['shortest_edge']
								elif w > h:
									snake_case_		:			Any											=	self.size['shortest_edge']
									snake_case_		:			Tuple											=	int(self.size['shortest_edge'] * w / h	)
								else:
									snake_case_		:			List[str]											=	self.size['shortest_edge']
									snake_case_		:			Optional[int]											=	self.size['shortest_edge']

							else:
								snake_case_		:			str											=	[]
								for image in image_inputs:
									snake_case_						,snake_case_		:			List[Any]											=	self.get_expected_values([image]	)
									expected_values.append((expected_height, expected_width)	)
								snake_case_		:			Dict											=	max(_a		,   key=lambda _A	: item[0]	)[0]
								snake_case_		:			int											=	max(_a		,   key=lambda _A	: item[1]	)[1]

							return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					__SCREAMING_SNAKE_CASE       ,				unittest.TestCase					):
						__magic_name__:			Optional[Any]       =       DeformableDetrImageProcessor if is_vision_available() else None






						def UpperCAmelCase_				(      self      :      int	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Dict											=	DeformableDetrImageProcessingTester(self	)






						@property
						def UpperCAmelCase_				(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_				(      self      :      str	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_a		,   'image_mean'	)	)
							self.assertTrue(hasattr(_a		,   'image_std'	)	)
							self.assertTrue(hasattr(_a		,   'do_normalize'	)	)
							self.assertTrue(hasattr(_a		,   'do_resize'	)	)
							self.assertTrue(hasattr(_a		,   'do_rescale'	)	)
							self.assertTrue(hasattr(_a		,   'do_pad'	)	)
							self.assertTrue(hasattr(_a		,   'size'	)	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size		,   {'shortest_edge': 18, 'longest_edge': 1333}	)
							self.assertEqual(image_processor.do_pad		,   _a	)

							snake_case_		:			Any											=	self.image_processing_class.from_dict(
							    self.image_processor_dict		,   size=42		,   max_size=84		,   pad_and_return_pixel_mask=_a	)
							self.assertEqual(image_processor.size		,   {'shortest_edge': 42, 'longest_edge': 84}	)
							self.assertEqual(image_processor.do_pad		,   _a	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[int]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    str:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							snake_case_		:			Dict											=	prepare_image_inputs(self.image_processor_tester		,   equal_resolution=_a	)
							for image in image_inputs:
								self.assertIsInstance(_a		,   Image.Image	)

							# Test not batched input
							snake_case_		:			List[Any]											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values

							snake_case_						,snake_case_		:			Union[str, Any]											=	self.image_processor_tester.get_expected_values(_a	)

							self.assertEqual(
							    encoded_images.shape		,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,   )

							# Test batched
							snake_case_						,snake_case_		:			Any											=	self.image_processor_tester.get_expected_values(_a		,   batched=_a	)

							snake_case_		:			List[str]											=	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,
							        expected_height,
							        expected_width,
							    )		,   )






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







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							snake_case_		:			str											=	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
							snake_case_		:			str											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values

							snake_case_						,snake_case_		:			Union[str, Any]											=	self.image_processor_tester.get_expected_values(_a	)

							self.assertEqual(
							    encoded_images.shape		,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,   )

							# Test batched
							snake_case_		:			Any											=	image_processing(_a		,   return_tensors='pt'	).pixel_values

							snake_case_						,snake_case_		:			Tuple											=	self.image_processor_tester.get_expected_values(_a		,   batched=_a	)

							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,   )






						def UpperCAmelCase_				(      self      :      str	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							snake_case_		:			Tuple											=	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
							snake_case_		:			Tuple											=	image_processing(image_inputs[0]		,   return_tensors='pt'	).pixel_values

							snake_case_						,snake_case_		:			Optional[Any]											=	self.image_processor_tester.get_expected_values(_a	)

							self.assertEqual(
							    encoded_images.shape		,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,   )

							# Test batched
							snake_case_		:			Any											=	image_processing(_a		,   return_tensors='pt'	).pixel_values

							snake_case_						,snake_case_		:			int											=	self.image_processor_tester.get_expected_values(_a		,   batched=_a	)

							self.assertEqual(
							    encoded_images.shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,   )






						@slow
						def UpperCAmelCase_				(      self      :      Tuple	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,   'r'	) as f:
								snake_case_		:			List[Any]											=	json.loads(f.read()	)

							snake_case_		:			Union[str, Any]											=	{'image_id': 39769, 'annotations': target}

							# encode them
							snake_case_		:			List[Any]											=	DeformableDetrImageProcessor()
							snake_case_		:			Optional[Any]											=	image_processing(images=_a		,   annotations=_a		,   return_tensors='pt'	)

							# verify pixel values
							snake_case_		:			Dict											=	torch.Size([1, 3, 800, 1066]	)
							self.assertEqual(encoding['pixel_values'].shape		,   _a	)

							snake_case_		:			Optional[Any]											=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,   _a		,   atol=1E-4	)	)

							# verify area
							snake_case_		:			Optional[Any]											=	torch.tensor([5_8_8_7.9_6_0_0, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,   _a	)	)
							# verify boxes
							snake_case_		:			List[str]											=	torch.Size([6, 4]	)
							self.assertEqual(encoding['labels'][0]['boxes'].shape		,   _a	)
							snake_case_		:			Tuple											=	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,   _a		,   atol=1E-3	)	)
							# verify image_id
							snake_case_		:			Tuple											=	torch.tensor([39769]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,   _a	)	)
							# verify is_crowd
							snake_case_		:			Optional[int]											=	torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,   _a	)	)
							# verify class_labels
							snake_case_		:			int											=	torch.tensor([75, 75, 63, 65, 17, 17]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,   _a	)	)
							# verify orig_size
							snake_case_		:			str											=	torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,   _a	)	)
							# verify size
							snake_case_		:			Tuple											=	torch.tensor([800, 1066]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,   _a	)	)






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







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,   'r'	) as f:
								snake_case_		:			int											=	json.loads(f.read()	)

							snake_case_		:			List[str]											=	{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							snake_case_		:			Tuple											=	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

							# encode them
							snake_case_		:			Optional[int]											=	DeformableDetrImageProcessor(format='coco_panoptic'	)
							snake_case_		:			Optional[int]											=	image_processing(images=_a		,   annotations=_a		,   masks_path=_a		,   return_tensors='pt'	)

							# verify pixel values
							snake_case_		:			int											=	torch.Size([1, 3, 800, 1066]	)
							self.assertEqual(encoding['pixel_values'].shape		,   _a	)

							snake_case_		:			Any											=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,   _a		,   atol=1E-4	)	)

							# verify area
							snake_case_		:			List[str]											=	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,   _a	)	)
							# verify boxes
							snake_case_		:			str											=	torch.Size([6, 4]	)
							self.assertEqual(encoding['labels'][0]['boxes'].shape		,   _a	)
							snake_case_		:			Any											=	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,   _a		,   atol=1E-3	)	)
							# verify image_id
							snake_case_		:			List[Any]											=	torch.tensor([39769]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,   _a	)	)
							# verify is_crowd
							snake_case_		:			Any											=	torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,   _a	)	)
							# verify class_labels
							snake_case_		:			int											=	torch.tensor([17, 17, 63, 75, 75, 93]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,   _a	)	)
							# verify masks
							snake_case_		:			Dict											=	822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,   _a	)
							# verify orig_size
							snake_case_		:			int											=	torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,   _a	)	)
							# verify size
							snake_case_		:			Optional[int]											=	torch.tensor([800, 1066]	)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,   _a	)	)








# 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


_SCREAMING_SNAKE_CASE      	=		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)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# 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
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	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 UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	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
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	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 UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

