
'''simple docstring'''

import random
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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		import PIL

		from transformers import FlavaImageProcessor
		from transformers.image_utils import PILImageResampling
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)
else:
		UpperCAmelCase__				:	List[Any]					=						None






class 				A       (		unittest.TestCase      ):





	def __init__(   self     :    Dict ,							__magic_name__     :    List[Any] ,							__magic_name__     :    List[Any]=7 ,							__magic_name__     :    str=3 ,							__magic_name__     :    Union[str, Any]=30 ,							__magic_name__     :    Any=400 ,							__magic_name__     :    str=True ,							__magic_name__     :    Optional[int]=None ,							__magic_name__     :    Union[str, Any]=True ,							__magic_name__     :    Optional[int]=None ,							__magic_name__     :    Optional[Any]=None ,							__magic_name__     :    List[str]=True ,							__magic_name__     :    str=1 / 255 ,							__magic_name__     :    Dict=True ,							__magic_name__     :    str=FLAVA_IMAGE_MEAN ,							__magic_name__     :    Optional[Any]=FLAVA_IMAGE_STD ,							__magic_name__     :    str=14 ,							__magic_name__     :    Optional[Any]=75 ,							__magic_name__     :    Any=None ,							__magic_name__     :    List[str]=16 ,							__magic_name__     :    Any=0.3 ,							__magic_name__     :    Union[str, Any]=None ,							__magic_name__     :    Optional[Any]=True ,							__magic_name__     :    Union[str, Any]=None ,							__magic_name__     :    int=None ,							__magic_name__     :    Optional[Any]=True ,							__magic_name__     :    str=None ,							__magic_name__     :    Optional[Any]=True ,							__magic_name__     :    Optional[int]=True ,							__magic_name__     :    Union[str, Any]=FLAVA_CODEBOOK_MEAN ,							__magic_name__     :    int=FLAVA_CODEBOOK_STD ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							size if size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__									=							crop_size if crop_size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__									=							codebook_size if codebook_size is not None else {"height": 112, "width": 112}
				lowerCAmelCase__									=							codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

				lowerCAmelCase__									=							parent
				lowerCAmelCase__									=							batch_size
				lowerCAmelCase__									=							num_channels
				lowerCAmelCase__									=							do_resize
				lowerCAmelCase__									=							do_rescale
				lowerCAmelCase__									=							rescale_factor
				lowerCAmelCase__									=							min_resolution
				lowerCAmelCase__									=							max_resolution
				lowerCAmelCase__									=							size
				lowerCAmelCase__									=							resample if resample is not None else PILImageResampling.BICUBIC
				lowerCAmelCase__									=							do_normalize
				lowerCAmelCase__									=							image_mean
				lowerCAmelCase__									=							image_std
				lowerCAmelCase__									=							do_center_crop
				lowerCAmelCase__									=							crop_size

				lowerCAmelCase__									=							input_size_patches
				lowerCAmelCase__									=							total_mask_patches
				lowerCAmelCase__									=							mask_group_max_patches
				lowerCAmelCase__									=							mask_group_min_patches
				lowerCAmelCase__									=							mask_group_min_aspect_ratio
				lowerCAmelCase__									=							mask_group_max_aspect_ratio

				lowerCAmelCase__									=							codebook_do_resize
				lowerCAmelCase__									=							codebook_size
				lowerCAmelCase__									=							codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
				lowerCAmelCase__									=							codebook_do_center_crop
				lowerCAmelCase__									=							codebook_crop_size
				lowerCAmelCase__									=							codebook_do_map_pixels
				lowerCAmelCase__									=							codebook_do_normalize
				lowerCAmelCase__									=							codebook_image_mean
				lowerCAmelCase__									=							codebook_image_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "resample": self.resample,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "input_size_patches": self.input_size_patches,
				    "total_mask_patches": self.total_mask_patches,
				    "mask_group_max_patches": self.mask_group_max_patches,
				    "mask_group_min_patches": self.mask_group_min_patches,
				    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "codebook_do_resize": self.codebook_do_resize,
				    "codebook_size": self.codebook_size,
				    "codebook_resample": self.codebook_resample,
				    "codebook_do_center_crop": self.codebook_do_center_crop,
				    "codebook_crop_size": self.codebook_crop_size,
				    "codebook_do_map_pixels": self.codebook_do_map_pixels,
				    "codebook_do_normalize": self.codebook_do_normalize,
				    "codebook_image_mean": self.codebook_image_mean,
				    "codebook_image_std": self.codebook_image_std,
				}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				return (self.size["height"], self.size["width"])





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				return (
				    (self.input_size_patches, self.input_size_patches)
				    if not isinstance(self.input_size_patches ,							__magic_name__							)
				    else self.input_size_patches
				)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class 				A       (		SCREAMING_SNAKE_CASE__					, unittest.TestCase      ):
	snake_case__ :List[str]				       =       FlavaImageProcessor if is_vision_available() else None
	snake_case__ :int				       =       None





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							FlavaImageProcessingTester(self							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__magic_name__ ,							"image_mean"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"image_std"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"do_normalize"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"do_resize"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"resample"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"crop_size"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"do_center_crop"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"do_rescale"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"rescale_factor"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"masking_generator"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_do_resize"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_size"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_resample"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_do_center_crop"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_crop_size"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_do_map_pixels"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_do_normalize"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_image_mean"							)							)
				self.assertTrue(hasattr(__magic_name__ ,							"codebook_image_std"							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size ,							{"height": 224, "width": 224}							)
				self.assertEqual(image_processor.crop_size ,							{"height": 224, "width": 224}							)
				self.assertEqual(image_processor.codebook_size ,							{"height": 112, "width": 112}							)
				self.assertEqual(image_processor.codebook_crop_size ,							{"height": 112, "width": 112}							)

				lowerCAmelCase__									=							self.image_processing_class.from_dict(
				    self.image_processor_dict ,							size=42 ,							crop_size=84 ,							codebook_size=33 ,							codebook_crop_size=66							)
				self.assertEqual(image_processor.size ,							{"height": 42, "width": 42}							)
				self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}							)
				self.assertEqual(image_processor.codebook_size ,							{"height": 33, "width": 33}							)
				self.assertEqual(image_processor.codebook_crop_size ,							{"height": 66, "width": 66}							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				pass





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

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

				# Test not batched input
				lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_tensors="pt"							)

				# Test no bool masked pos
				self.assertFalse("bool_masked_pos" in encoded_images							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_image_size()

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

				# Test batched
				lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_tensors="pt"							)
				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_image_size()

				# Test no bool masked pos
				self.assertFalse("bool_masked_pos" in encoded_images							)

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
				# create random tensors
				lowerCAmelCase__									=							prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							**__magic_name__							)
				for image in image_inputs:
							self.assertIsInstance(__magic_name__ ,							__magic_name__							)

				# Test not batched input
				lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_tensors="pt"							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,							)

				lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_image_mask=__magic_name__ ,							return_tensors="pt"							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape ,							(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) ,							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape ,							(
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    ) ,							)

				# Test batched
				lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_tensors="pt"							).pixel_values

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

				# Test masking
				lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_image_mask=__magic_name__ ,							return_tensors="pt"							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape ,							(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) ,							)

				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape ,							(
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    ) ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				self._test_call_framework(np.ndarray ,							prepare_kwargs={"numpify": True}							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				self._test_call_framework(torch.Tensor ,							prepare_kwargs={"torchify": True}							)





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


				"""simple docstring"""

				random.seed(1234							)
				lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
				lowerCAmelCase__									=							prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							torchify=__magic_name__							)

				# Test not batched input
				lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_image_mask=__magic_name__ ,							return_tensors="pt"							)
				self.assertEqual(encoded_images.bool_masked_pos.sum().item() ,							75							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

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

				# Test not batched input
				lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_codebook_pixels=__magic_name__ ,							return_tensors="pt"							)
				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,							)

				# Test batched
				lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_codebook_pixels=__magic_name__ ,							return_tensors="pt"							)
				lowerCAmelCase__		,lowerCAmelCase__									=							self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape ,							(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    ) ,							)







'''simple docstring'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





