




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase			:	int							 =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class        UpperCamelCase_					(				nn.Module  ):

				'''simple docstring'''






				def __init__(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			int)		->int:





											'''simple docstring'''

											super().__init__()
											A__   				=					torchvision.models.resnetaaa(pretrained=UpperCAmelCase__)
											A__   				=					list(model.children())[:-2]
											A__   				=					nn.Sequential(*UpperCAmelCase__)
											A__   				=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])





				def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





											'''simple docstring'''

											A__   				=					self.pool(self.model(UpperCAmelCase__))
											A__   				=					torch.flatten(UpperCAmelCase__      ,				start_dim=2)
											A__   				=					out.transpose(1      ,				2).contiguous()
											return out  # BxNx2048


class        UpperCamelCase_					(				UpperCAmelCase__  ):

				'''simple docstring'''






				def __init__(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[Any])		->Dict:





											'''simple docstring'''

											A__   				=					[json.loads(UpperCAmelCase__) for l in open(UpperCAmelCase__)]
											A__   				=					os.path.dirname(UpperCAmelCase__)
											A__   				=					tokenizer
											A__   				=					labels
											A__   				=					len(UpperCAmelCase__)
											A__   				=					max_seq_length

											A__   				=					transforms


				def __len__(    self   :			List[Any])		->List[str]:





											'''simple docstring'''

											return len(self.data)


				def __getitem__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict)		->Dict:





											'''simple docstring'''

											A__   				=					torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']      ,				add_special_tokens=UpperCAmelCase__))
											A__				,							A__				,							A__   				=					sentence[0], sentence[1:-1], sentence[-1]
											A__   				=					sentence[: self.max_seq_length]

											A__   				=					torch.zeros(self.n_classes)
											A__   				=					1

											A__   				=					Image.open(os.path.join(self.data_dir      ,				self.data[index]['''img'''])).convert('''RGB''')
											A__   				=					self.transforms(UpperCAmelCase__)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}





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





											'''simple docstring'''

											A__   				=					Counter()
											for row in self.data:
																		label_freqs.update(row['''label'''])
											return label_freqs







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



							"""simple docstring"""

							A__   				=					[len(row['''sentence''']		) for row in batch]
							A__				,							A__   				=					len(lowercase_		), max(lowercase_		)

							A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)
							A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)

							for i_batch, (input_row, length) in enumerate(zip(lowercase_				,       lowercase_		)		):
														A__   				=					input_row['''sentence''']
														A__   				=					1

							A__   				=					torch.stack([row['''image'''] for row in batch]		)
							A__   				=					torch.stack([row['''label'''] for row in batch]		)
							A__   				=					torch.stack([row['''image_start_token'''] for row in batch]		)
							A__   				=					torch.stack([row['''image_end_token'''] for row in batch]		)

							return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def SCREAMING_SNAKE_CASE       ( )     -> str:



							"""simple docstring"""

							return [
							    "Crime",
							    "Drama",
							    "Thriller",
							    "Action",
							    "Comedy",
							    "Romance",
							    "Documentary",
							    "Short",
							    "Mystery",
							    "History",
							    "Family",
							    "Adventure",
							    "Fantasy",
							    "Sci-Fi",
							    "Western",
							    "Horror",
							    "Sport",
							    "War",
							    "Music",
							    "Musical",
							    "Animation",
							    "Biography",
							    "Film-Noir",
							]







def SCREAMING_SNAKE_CASE       ( )     -> List[Any]:



							"""simple docstring"""

							return transforms.Compose(
							    [
							        transforms.Resize(256		),
							        transforms.CenterCrop(224		),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
							    ]		)








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''output_type''': '''numpy''',
           }

           return inputs


    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()      ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,				)
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1e-1)


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





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


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





           '''simple docstring'''

           self._test_save_load_local()





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





           '''simple docstring'''

           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2      ,				)


