






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			KandinskyInpaintPipeline
				_snake_case				:   int        =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				_snake_case				:   Any        =			[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				_snake_case				:   List[Any]        =			[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				_snake_case				:   Tuple        =			False







				@property
				def 				__a   ( self     :Optional[int]     ):
					return 32







				@property
				def 				__a   ( self     :Any     ):
					return 32







				@property
				def 				__a   ( self     :str     ):
					return self.time_input_dim







				@property
				def 				__a   ( self     :List[Any]     ):
					return self.time_input_dim * 4







				@property
				def 				__a   ( self     :Dict     ):
					return 1_00







				@property
				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Any						=      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""     )
					return tokenizer







				@property
				def 				__a   ( self     :Dict     ):
					torch.manual_seed(0     )
					UpperCamelCase__ :Union[str, Any]						=      MCLIPConfig(
					    numDims=self.cross_attention_dim  ,     transformerDimensions=self.text_embedder_hidden_size  ,     hidden_size=self.text_embedder_hidden_size  ,     intermediate_size=37  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     vocab_size=10_05  ,     )

					UpperCamelCase__ :Optional[Any]						=      MultilingualCLIP(lowerCamelCase__     )
					UpperCamelCase__ :Optional[Any]						=      text_encoder.eval()

					return text_encoder







				@property
				def 				__a   ( self     :Dict     ):
					torch.manual_seed(0     )

					UpperCamelCase__ :Optional[Any]						=      {
					    """in_channels""": 9,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 8,
					    """addition_embed_type""": """text_image""",
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """encoder_hid_dim""": self.text_embedder_hidden_size,
					    """encoder_hid_dim_type""": """text_image_proj""",
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": None,
					}

					UpperCamelCase__ :Optional[Any]						=      UNetaDConditionModel(**lowerCamelCase__     )
					return model







				@property
				def 				__a   ( self     :List[Any]     ):
					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}







				@property
				def 				__a   ( self     :str     ):
					torch.manual_seed(0     )
					UpperCamelCase__ :Tuple						=      VQModel(**self.dummy_movq_kwargs     )
					return model







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      self.dummy_text_encoder
					UpperCamelCase__ :str						=      self.dummy_tokenizer
					UpperCamelCase__ :List[str]						=      self.dummy_unet
					UpperCamelCase__ :Any						=      self.dummy_movq

					UpperCamelCase__ :Optional[int]						=      DDIMScheduler(
					    num_train_timesteps=10_00  ,     beta_schedule="""linear"""  ,     beta_start=0.0_0085  ,     beta_end=0.012  ,     clip_sample=lowerCamelCase__  ,     set_alpha_to_one=lowerCamelCase__  ,     steps_offset=1  ,     prediction_type="""epsilon"""  ,     thresholding=lowerCamelCase__  ,     )

					UpperCamelCase__ :Optional[int]						=      {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :int=0     ):
					UpperCamelCase__ :Union[str, Any]						=      floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
					UpperCamelCase__ :int						=      floats_tensor((1, self.cross_attention_dim)  ,     rng=random.Random(seed + 1     )     ).to(lowerCamelCase__     )
					# create init_image
					UpperCamelCase__ :Optional[int]						=      floats_tensor((1, 3, 64, 64)  ,     rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
					UpperCamelCase__ :str						=      image.cpu().permute(0  ,     2  ,     3  ,     1     )[0]
					UpperCamelCase__ :Any						=      Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )
					# create mask
					UpperCamelCase__ :Any						=      np.ones((64, 64)  ,     dtype=np.floataa     )
					UpperCamelCase__ :Optional[int]						=      0

					if str(lowerCamelCase__     ).startswith("""mps"""     ):
						UpperCamelCase__ :List[str]						=      torch.manual_seed(lowerCamelCase__     )
					else:
						UpperCamelCase__ :str						=      torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
					UpperCamelCase__ :List[str]						=      {
					    """prompt""": """horse""",
					    """image""": init_image,
					    """mask_image""": mask,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 4.0,
					    """output_type""": """np""",
					}
					return inputs







				def 				__a   ( self     :str     ):
					UpperCamelCase__ :Dict						=      """cpu"""

					UpperCamelCase__ :Any						=      self.get_dummy_components()

					UpperCamelCase__ :List[Any]						=      self.pipeline_class(**lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      pipe.to(lowerCamelCase__     )

					pipe.set_progress_bar_config(disable=lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      pipe(**self.get_dummy_inputs(lowerCamelCase__     )     )
					UpperCamelCase__ :List[str]						=      output.images

					UpperCamelCase__ :str						=      pipe(
					    **self.get_dummy_inputs(lowerCamelCase__     )  ,     return_dict=lowerCamelCase__  ,     )[0]

					UpperCamelCase__ :str						=      image[0, -3:, -3:, -1]
					UpperCamelCase__ :Union[str, Any]						=      image_from_tuple[0, -3:, -3:, -1]

					print(f"""image.shape {image.shape}"""     )

					assert image.shape == (1, 64, 64, 3)

					UpperCamelCase__ :Optional[int]						=      np.array(
					    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]     )

					assert (
					    np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
					), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
					), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







				def 				__a   ( self     :Optional[Any]     ):
					super().test_inference_batch_single_identical(expected_max_diff=3e-3     )

@slow
@require_torch_gpu
class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				def 				__a   ( self     :Tuple     ):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""     )

					UpperCamelCase__ :List[str]						=      load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""     )
					UpperCamelCase__ :List[Any]						=      np.ones((7_68, 7_68)  ,     dtype=np.floataa     )
					UpperCamelCase__ :Dict						=      0

					UpperCamelCase__ :Optional[int]						=      """a hat"""

					UpperCamelCase__ :Dict						=      KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""  ,     torch_dtype=torch.floataa     )
					pipe_prior.to(lowerCamelCase__     )

					UpperCamelCase__ :Union[str, Any]						=      KandinskyInpaintPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-inpaint"""  ,     torch_dtype=torch.floataa     )
					UpperCamelCase__ :Optional[Any]						=      pipeline.to(lowerCamelCase__     )
					pipeline.set_progress_bar_config(disable=lowerCamelCase__     )

					UpperCamelCase__ :Optional[Any]						=      torch.Generator(device="""cpu"""     ).manual_seed(0     )
					UpperCamelCase__				,		UpperCamelCase__ :str						=      pipe_prior(
					    lowerCamelCase__  ,     generator=lowerCamelCase__  ,     num_inference_steps=5  ,     negative_prompt=""""""  ,     ).to_tuple()

					UpperCamelCase__ :int						=      pipeline(
					    lowerCamelCase__  ,     image=lowerCamelCase__  ,     mask_image=lowerCamelCase__  ,     image_embeds=lowerCamelCase__  ,     negative_image_embeds=lowerCamelCase__  ,     generator=lowerCamelCase__  ,     num_inference_steps=1_00  ,     height=7_68  ,     width=7_68  ,     output_type="""np"""  ,     )

					UpperCamelCase__ :List[str]						=      output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(lowerCamelCase__  ,     lowerCamelCase__     )

import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a         =   'src/transformers'
__a         =   'docs/source/en/tasks'




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				with open(snake_case__		,  '''r'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
								lowercase_	    =   f.readlines()
				# Find the start prompt.
				lowercase_	    =   0
				while not lines[start_index].startswith(snake_case__   ):
								start_index += 1
				start_index += 1

				lowercase_	    =   start_index
				while not lines[end_index].startswith(snake_case__   ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]   ) <= 1:
								start_index += 1
				while len(lines[end_index]   ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a         =   direct_transformers_import(TRANSFORMERS_PATH)

__a         =   {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a         =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]   ) + "\n"




def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   _find_text_in_file(
				    filename=os.path.join(snake_case__		,  snake_case__   )		,  start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''		,  end_prompt='''<!--End of the generated tip-->'''		,  )

				lowercase_	    =   get_model_list_for_task(snake_case__   )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(snake_case__		,  snake_case__   )		,  '''w'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
								else:
												raise ValueError(
												    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
												    ''' to fix this.'''   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a         =   parser.parse_args()

						for task_guide in TASK_GUIDE_TO_MODELS.keys():
												check_model_list_for_task(task_guide, args.fix_and_overwrite)




