
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		,  ):
				__magic_name__						:						Union[str, Any]        =	size if size is not None else {'''height''': 20, '''width''': 20}
				__magic_name__						:						List[Any]        =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
				__magic_name__						:						Optional[Any]        =	parent
				__magic_name__						:						Optional[int]        =	batch_size
				__magic_name__						:						Tuple        =	num_channels
				__magic_name__						:						Optional[Any]        =	image_size
				__magic_name__						:						List[Any]        =	min_resolution
				__magic_name__						:						int        =	max_resolution
				__magic_name__						:						Union[str, Any]        =	do_resize
				__magic_name__						:						Dict        =	size
				__magic_name__						:						List[str]        =	do_center_crop
				__magic_name__						:						Dict        =	crop_size
				__magic_name__						:						Any        =	do_normalize
				__magic_name__						:						Tuple        =	image_mean
				__magic_name__						:						Tuple        =	image_std
				__magic_name__						:						Any        =	do_reduce_labels







			def      SCREAMING_SNAKE_CASE       (							self						):
				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_		(   )							->						List[str]:







	'''simple docstring'''


	__magic_name__						:						List[Any]        =	load_dataset("hf-internal-testing/fixtures_ade20k" ,  split="test"  )

	__magic_name__						:						Tuple        =	Image.open(dataset[0]["file"]  )
	__magic_name__						:						str        =	Image.open(dataset[1]["file"]  )

	return image, map






def     lowerCAmelCase_		(   )							->						List[Any]:







	'''simple docstring'''


	__magic_name__						:						str        =	load_dataset("hf-internal-testing/fixtures_ade20k" ,  split="test"  )

	__magic_name__						:						List[str]        =	Image.open(ds[0]["file"]  )
	__magic_name__						:						str        =	Image.open(ds[1]["file"]  )
	__magic_name__						:						str        =	Image.open(ds[2]["file"]  )
	__magic_name__						:						Tuple        =	Image.open(ds[3]["file"]  )

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





@require_torch
@require_vision
class  _snake_case   (	UpperCamelCase_							,		unittest.TestCase							):
			UpperCamelCase__        =       BeitImageProcessor if is_vision_available() else None







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Tuple        =	BeitImageProcessingTester(self						)







			@property
			def      SCREAMING_SNAKE_CASE       (							self						):
				return self.image_processor_tester.prepare_image_processor_dict()







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						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"						)						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[Any]        =	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		,  __SCREAMING_SNAKE_CASE						)

				__magic_name__						:						Optional[int]        =	self.image_processing_class.from_dict(
				    self.image_processor_dict		,  size=42		,  crop_size=84		,  reduce_labels=__SCREAMING_SNAKE_CASE						)
				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		,  __SCREAMING_SNAKE_CASE						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				pass







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						int        =	self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__magic_name__						:						Optional[Any]        =	prepare_image_inputs(self.image_processor_tester		,  equal_resolution=__SCREAMING_SNAKE_CASE						)
				for image in image_inputs:
					self.assertIsInstance(__SCREAMING_SNAKE_CASE		,  Image.Image						)

				# Test not batched input
				__magic_name__						:						Union[str, 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
				__magic_name__						:						Optional[int]        =	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      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[Any]        =	self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__magic_name__						:						Optional[Any]        =	prepare_image_inputs(self.image_processor_tester		,  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
				__magic_name__						:						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
				__magic_name__						:						List[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"],
				    )		,  )







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Union[str, Any]        =	self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__magic_name__						:						str        =	prepare_image_inputs(self.image_processor_tester		,  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
				__magic_name__						:						Optional[int]        =	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
				__magic_name__						:						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      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Dict        =	self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__magic_name__						:						Optional[int]        =	prepare_image_inputs(self.image_processor_tester		,  equal_resolution=__SCREAMING_SNAKE_CASE		,  torchify=__SCREAMING_SNAKE_CASE						)
				__magic_name__						:						int        =	[]
				for image in image_inputs:
					self.assertIsInstance(__SCREAMING_SNAKE_CASE		,  torch.Tensor						)
					maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__magic_name__						:						str        =	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
				__magic_name__						:						Dict        =	image_processing(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE		,  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)
				__magic_name__						:						Any        =	prepare_semantic_single_inputs()

				__magic_name__						:						Union[str, Any]        =	image_processing(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE		,  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)
				__magic_name__						:						Union[str, Any]        =	prepare_semantic_batch_inputs()

				__magic_name__						:						Any        =	image_processing(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE		,  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						):
				__magic_name__						:						Tuple        =	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
				__magic_name__						:						Optional[Any]        =	prepare_semantic_single_inputs()
				__magic_name__						:						Dict        =	image_processing(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE		,  return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__magic_name__						:						str        =	True
				__magic_name__						:						Union[str, Any]        =	image_processing(__SCREAMING_SNAKE_CASE		,  __SCREAMING_SNAKE_CASE		,  return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


