

'''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:
					_lowerCamelCase	 =			_lowerCamelCase	 =			_lowerCamelCase	 =			_lowerCamelCase	 =			None
class  _snake_case						(unittest.TestCase):






			def __init__(   self       ,_snake_case       ,_snake_case=7       ,_snake_case=3       ,_snake_case=30       ,_snake_case=4_00       ,_snake_case=True       ,_snake_case=None       ,_snake_case=True       ,_snake_case=None       ,_snake_case=None       ,_snake_case=True       ,_snake_case=1 / 2_55       ,_snake_case=True       ,_snake_case=FLAVA_IMAGE_MEAN       ,_snake_case=FLAVA_IMAGE_STD       ,_snake_case=14       ,_snake_case=75       ,_snake_case=None       ,_snake_case=16       ,_snake_case=0.3       ,_snake_case=None       ,_snake_case=True       ,_snake_case=None       ,_snake_case=None       ,_snake_case=True       ,_snake_case=None       ,_snake_case=True       ,_snake_case=True       ,_snake_case=FLAVA_CODEBOOK_MEAN       ,_snake_case=FLAVA_CODEBOOK_STD       ,):
				UpperCAmelCase_			:	Tuple      =   size if size is not None else {"height": 2_24, "width": 2_24}
				UpperCAmelCase_			:	Tuple      =   crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
				UpperCAmelCase_			:	Dict      =   codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
				UpperCAmelCase_			:	Union[str, Any]      =   codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

				UpperCAmelCase_			:	List[str]      =   parent
				UpperCAmelCase_			:	Optional[int]      =   batch_size
				UpperCAmelCase_			:	List[Any]      =   num_channels
				UpperCAmelCase_			:	Tuple      =   do_resize
				UpperCAmelCase_			:	Optional[int]      =   do_rescale
				UpperCAmelCase_			:	Tuple      =   rescale_factor
				UpperCAmelCase_			:	Any      =   min_resolution
				UpperCAmelCase_			:	int      =   max_resolution
				UpperCAmelCase_			:	str      =   size
				UpperCAmelCase_			:	Optional[Any]      =   resample if resample is not None else PILImageResampling.BICUBIC
				UpperCAmelCase_			:	Dict      =   do_normalize
				UpperCAmelCase_			:	Optional[Any]      =   image_mean
				UpperCAmelCase_			:	str      =   image_std
				UpperCAmelCase_			:	Any      =   do_center_crop
				UpperCAmelCase_			:	List[Any]      =   crop_size

				UpperCAmelCase_			:	Union[str, Any]      =   input_size_patches
				UpperCAmelCase_			:	List[Any]      =   total_mask_patches
				UpperCAmelCase_			:	List[Any]      =   mask_group_max_patches
				UpperCAmelCase_			:	Union[str, Any]      =   mask_group_min_patches
				UpperCAmelCase_			:	List[Any]      =   mask_group_min_aspect_ratio
				UpperCAmelCase_			:	str      =   mask_group_max_aspect_ratio

				UpperCAmelCase_			:	List[Any]      =   codebook_do_resize
				UpperCAmelCase_			:	int      =   codebook_size
				UpperCAmelCase_			:	List[Any]      =   codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
				UpperCAmelCase_			:	Any      =   codebook_do_center_crop
				UpperCAmelCase_			:	Tuple      =   codebook_crop_size
				UpperCAmelCase_			:	Optional[int]      =   codebook_do_map_pixels
				UpperCAmelCase_			:	List[str]      =   codebook_do_normalize
				UpperCAmelCase_			:	Dict      =   codebook_image_mean
				UpperCAmelCase_			:	Tuple      =   codebook_image_std






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






			def 						UpperCamelCase__				(   self			):
				return (
				    (self.input_size_patches, self.input_size_patches)
				    if not isinstance(self.input_size_patches       ,_snake_case			)
				    else self.input_size_patches
				)






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




@require_torch
@require_vision
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     int          =FlavaImageProcessor if is_vision_available() else None
			__A   :     List[str]          =None






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[Any]      =   FlavaImageProcessingTester(self			)






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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[Any]      =   self.image_processing_class(**self.image_processor_dict			)
				self.assertTrue(hasattr(_snake_case       ,"image_mean"			)			)
				self.assertTrue(hasattr(_snake_case       ,"image_std"			)			)
				self.assertTrue(hasattr(_snake_case       ,"do_normalize"			)			)
				self.assertTrue(hasattr(_snake_case       ,"do_resize"			)			)
				self.assertTrue(hasattr(_snake_case       ,"resample"			)			)
				self.assertTrue(hasattr(_snake_case       ,"crop_size"			)			)
				self.assertTrue(hasattr(_snake_case       ,"do_center_crop"			)			)
				self.assertTrue(hasattr(_snake_case       ,"do_rescale"			)			)
				self.assertTrue(hasattr(_snake_case       ,"rescale_factor"			)			)
				self.assertTrue(hasattr(_snake_case       ,"masking_generator"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_do_resize"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_size"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_resample"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_do_center_crop"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_crop_size"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_do_map_pixels"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_do_normalize"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_image_mean"			)			)
				self.assertTrue(hasattr(_snake_case       ,"codebook_image_std"			)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[Any]      =   self.image_processing_class.from_dict(self.image_processor_dict			)
				self.assertEqual(image_processor.size       ,{"height": 2_24, "width": 2_24}			)
				self.assertEqual(image_processor.crop_size       ,{"height": 2_24, "width": 2_24}			)
				self.assertEqual(image_processor.codebook_size       ,{"height": 1_12, "width": 1_12}			)
				self.assertEqual(image_processor.codebook_crop_size       ,{"height": 1_12, "width": 1_12}			)

				UpperCAmelCase_			:	str      =   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 						UpperCamelCase__				(   self			):
				pass






			def 						UpperCamelCase__				(   self			):
				# Initialize image_processing
				UpperCAmelCase_			:	int      =   self.image_processing_class(**self.image_processor_dict			)
				# create random PIL images
				UpperCAmelCase_			:	Optional[int]      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,PIL.Image.Image			)

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

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

				UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   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
				UpperCAmelCase_			:	Optional[int]      =   image_processing(_snake_case       ,return_tensors="pt"			)
				UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   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 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):
				# Initialize image_processing
				UpperCAmelCase_			:	Dict      =   self.image_processing_class(**self.image_processor_dict			)
				# create random tensors
				UpperCAmelCase_			:	Tuple      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case       ,**_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,_snake_case			)

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

				UpperCAmelCase_ , UpperCAmelCase_			:	str      =   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)       ,)

				UpperCAmelCase_			:	List[Any]      =   image_processing(_snake_case       ,return_image_mask=_snake_case       ,return_tensors="pt"			)

				UpperCAmelCase_ , UpperCAmelCase_			:	int      =   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,
				    )       ,)

				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[int]      =   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
				UpperCAmelCase_			:	Tuple      =   image_processing(_snake_case       ,return_tensors="pt"			).pixel_values

				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[int]      =   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
				UpperCAmelCase_			:	Union[str, Any]      =   image_processing(_snake_case       ,return_image_mask=_snake_case       ,return_tensors="pt"			)

				UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   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,
				    )       ,)

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






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






			def 						UpperCamelCase__				(   self			):
				# Initialize image_processing
				random.seed(12_34			)
				UpperCAmelCase_			:	Tuple      =   self.image_processing_class(**self.image_processor_dict			)
				UpperCAmelCase_			:	Optional[Any]      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case       ,torchify=_snake_case			)

				# Test not batched input
				UpperCAmelCase_			:	List[Any]      =   image_processing(image_inputs[0]       ,return_image_mask=_snake_case       ,return_tensors="pt"			)
				self.assertEqual(encoded_images.bool_masked_pos.sum().item()       ,75			)






			def 						UpperCamelCase__				(   self			):
				# Initialize image_processing
				UpperCAmelCase_			:	Tuple      =   self.image_processing_class(**self.image_processor_dict			)
				# create random PIL images
				UpperCAmelCase_			:	Any      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case			)
				for image in image_inputs:
					self.assertIsInstance(_snake_case       ,PIL.Image.Image			)

				# Test not batched input
				UpperCAmelCase_			:	Optional[int]      =   image_processing(image_inputs[0]       ,return_codebook_pixels=_snake_case       ,return_tensors="pt"			)
				UpperCAmelCase_ , UpperCAmelCase_			:	Any      =   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
				UpperCAmelCase_			:	str      =   image_processing(_snake_case       ,return_codebook_pixels=_snake_case       ,return_tensors="pt"			)
				UpperCAmelCase_ , UpperCAmelCase_			:	str      =   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"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

			__A       						=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



