


'''simple docstring'''
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class        lowerCAmelCase_  :






       def   UpperCamelCase						( self   )->      str:
            torch.manual_seed(0   )
            _A				=							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

            torch.manual_seed(0   )
            _A				=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

            torch.manual_seed(0   )
            _A				=							UNetaDConditionModel(
                sample_size=32			,     layers_per_block=1			,     block_out_channels=[32, 64]			,     down_block_types=[
                    'ResnetDownsampleBlock2D',
                    'SimpleCrossAttnDownBlock2D',
                ]			,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'			,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']			,     in_channels=3			,     out_channels=6			,     cross_attention_dim=32			,     encoder_hid_dim=32			,     attention_head_dim=8			,     addition_embed_type='text'			,     addition_embed_type_num_heads=2			,     cross_attention_norm='group_norm'			,     resnet_time_scale_shift='scale_shift'			,     act_fn='gelu'			,     )
            unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

            torch.manual_seed(0   )
            _A				=							DDPMScheduler(
                num_train_timesteps=1000			,     beta_schedule='squaredcos_cap_v2'			,     beta_start=0.0001			,     beta_end=0.02			,     thresholding=_UpperCamelCase			,     dynamic_thresholding_ratio=0.95			,     sample_max_value=1.0			,     prediction_type='epsilon'			,     variance_type='learned_range'			,     )

            torch.manual_seed(0   )
            _A				=							IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }






       def   UpperCamelCase						( self   )->      Optional[Any]:
            torch.manual_seed(0   )
            _A				=							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

            torch.manual_seed(0   )
            _A				=							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

            torch.manual_seed(0   )
            _A				=							UNetaDConditionModel(
                sample_size=32			,     layers_per_block=[1, 2]			,     block_out_channels=[32, 64]			,     down_block_types=[
                    'ResnetDownsampleBlock2D',
                    'SimpleCrossAttnDownBlock2D',
                ]			,     mid_block_type='UNetMidBlock2DSimpleCrossAttn'			,     up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']			,     in_channels=6			,     out_channels=6			,     cross_attention_dim=32			,     encoder_hid_dim=32			,     attention_head_dim=8			,     addition_embed_type='text'			,     addition_embed_type_num_heads=2			,     cross_attention_norm='group_norm'			,     resnet_time_scale_shift='scale_shift'			,     act_fn='gelu'			,     class_embed_type='timestep'			,     mid_block_scale_factor=1.414			,     time_embedding_act_fn='gelu'			,     time_embedding_dim=32			,     )
            unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

            torch.manual_seed(0   )
            _A				=							DDPMScheduler(
                num_train_timesteps=1000			,     beta_schedule='squaredcos_cap_v2'			,     beta_start=0.0001			,     beta_end=0.02			,     thresholding=_UpperCamelCase			,     dynamic_thresholding_ratio=0.95			,     sample_max_value=1.0			,     prediction_type='epsilon'			,     variance_type='learned_range'			,     )

            torch.manual_seed(0   )
            _A				=							DDPMScheduler(
                num_train_timesteps=1000			,     beta_schedule='squaredcos_cap_v2'			,     beta_start=0.0001			,     beta_end=0.02			,     )

            torch.manual_seed(0   )
            _A				=							IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.get_dummy_components()
            _A				=							self.pipeline_class(**_UpperCamelCase   )
            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )

            _A				=							inputs['prompt']
            _A				=							inputs['generator']
            _A				=							inputs['num_inference_steps']
            _A				=							inputs['output_type']

            if "image" in inputs:
                 _A				=							inputs['image']
            else:
                 _A				=							None

            if "mask_image" in inputs:
                 _A				=							inputs['mask_image']
            else:
                 _A				=							None

            if "original_image" in inputs:
                 _A				=							inputs['original_image']
            else:
                 _A				=							None

            _A		,				_A				=							pipe.encode_prompt(_UpperCamelCase   )

            # inputs with prompt converted to embeddings
            _A				=							{
                'prompt_embeds': prompt_embeds,
                'negative_prompt_embeds': negative_prompt_embeds,
                'generator': generator,
                'num_inference_steps': num_inference_steps,
                'output_type': output_type,
            }

            if image is not None:
                 _A				=							image

            if mask_image is not None:
                 _A				=							mask_image

            if original_image is not None:
                 _A				=							original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                 setattr(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )

            _A				=							pipe(**_UpperCamelCase   )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(_UpperCamelCase   )
                 _A				=							self.pipeline_class.from_pretrained(_UpperCamelCase   )
                 pipe_loaded.to(_UpperCamelCase   )
                 pipe_loaded.set_progress_bar_config(disable=_UpperCamelCase   )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

            for optional_component in pipe._optional_components:
                 self.assertTrue(
                     getattr(_UpperCamelCase			,     _UpperCamelCase   ) is None			,     F'`{optional_component}` did not stay set to None after loading.'			,     )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )

            _A				=							inputs['generator']
            _A				=							inputs['num_inference_steps']
            _A				=							inputs['output_type']

            # inputs with prompt converted to embeddings
            _A				=							{
                'prompt_embeds': prompt_embeds,
                'negative_prompt_embeds': negative_prompt_embeds,
                'generator': generator,
                'num_inference_steps': num_inference_steps,
                'output_type': output_type,
            }

            if image is not None:
                 _A				=							image

            if mask_image is not None:
                 _A				=							mask_image

            if original_image is not None:
                 _A				=							original_image

            _A				=							pipe_loaded(**_UpperCamelCase   )[0]

            _A				=							np.abs(to_np(_UpperCamelCase   ) - to_np(_UpperCamelCase   )   ).max()
            self.assertLess(_UpperCamelCase			,     1e-4   )






       def   UpperCamelCase						( self   )->      int:
            _A				=							self.get_dummy_components()
            _A				=							self.pipeline_class(**_UpperCamelCase   )
            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(_UpperCamelCase   )
                 _A				=							self.pipeline_class.from_pretrained(_UpperCamelCase   )
                 pipe_loaded.to(_UpperCamelCase   )
                 pipe_loaded.set_progress_bar_config(disable=_UpperCamelCase   )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe_loaded(**_UpperCamelCase   )[0]

            _A				=							np.abs(to_np(_UpperCamelCase   ) - to_np(_UpperCamelCase   )   ).max()
            self.assertLess(_UpperCamelCase			,     1e-4   )









'''simple docstring'''
import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCAmelCase						=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=18			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      Dict:
            _A				=							size if size is not None else {'height': 20, 'width': 20}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							image_size
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							size
            _A				=							do_normalize
            _A				=							do_convert_rgb
            _A				=							[512, 1024, 2048, 4096]
            _A				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}






       def   UpperCamelCase						( self   )->      Dict:
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
            _A				=							Image.open(requests.get(_UpperCamelCase			,     stream=_UpperCamelCase   ).raw   ).convert('RGB'   )
            return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							PixaStructImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processor_tester.prepare_dummy_image()

            _A				=							self.image_processing_class(**self.image_processor_dict   )
            _A				=							2048

            _A				=							image_processor(_UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   )
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,     torch.tensor(0.0606   )			,     atol=1e-3			,     rtol=1e-3   )   )






       def   UpperCamelCase						( self   )->      int:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            _A				=							True

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(_UpperCamelCase   ):
                      _A				=							image_processor(
                          image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches

                 _A				=							'Hello'

                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     np.ndarray   )

            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     torch.Tensor   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      str:
            _A				=							PixaStructImageProcessingTester(self			,     num_channels=4   )
            _A				=							3






       @property
       def   UpperCamelCase						( self   )->      str:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )





