






from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


A        =			logging.get_logger(__name__)  # pylint: disable=invalid-name

A        =			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]=8      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				SCREAMING_SNAKE_CASE_   :	int													=   w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor


class 							__a							(  __A						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    ):
						super().__init__()

						self.register_modules(
						    text_encoder=UpperCamelCase__ ,    tokenizer=UpperCamelCase__ ,    unet=UpperCamelCase__ ,    scheduler=UpperCamelCase__ ,    movq=UpperCamelCase__ ,    )
						SCREAMING_SNAKE_CASE_   :	int													=   2 ** (len(self.movq.config.block_out_channels       ) - 1)


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if latents is None:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   randn_tensor(UpperCamelCase__ ,    generator=UpperCamelCase__ ,    device=UpperCamelCase__ ,    dtype=UpperCamelCase__       )
						else:
										if latents.shape != shape:
														raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
										SCREAMING_SNAKE_CASE_   :	str													=   latents.to(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   latents * scheduler.init_noise_sigma
						return latents


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=None ,    ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(UpperCamelCase__       ) if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ) else 1
						# get prompt text embeddings
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.tokenizer(
						    UpperCamelCase__ ,    padding='max_length' ,    truncation=UpperCamelCase__ ,    max_length=77 ,    return_attention_mask=UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__ ,    return_tensors='pt' ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   text_inputs.input_ids
						SCREAMING_SNAKE_CASE_   :	int													=   self.tokenizer(UpperCamelCase__ ,    padding='longest' ,    return_tensors='pt'       ).input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	List[str]													=   self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]       )
										logger.warning(
										    'The following part of your input was truncated because CLIP can only handle sequences up to'
										    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   text_input_ids.to(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   text_inputs.attention_mask.to(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.text_encoder(
						    input_ids=UpperCamelCase__ ,    attention_mask=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   prompt_embeds.repeat_interleave(UpperCamelCase__ ,    dim=0       )
						SCREAMING_SNAKE_CASE_   :	int													=   text_encoder_hidden_states.repeat_interleave(UpperCamelCase__ ,    dim=0       )
						SCREAMING_SNAKE_CASE_   :	str													=   text_mask.repeat_interleave(UpperCamelCase__ ,    dim=0       )

						if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE_   :	List[str]
										if negative_prompt is None:
														SCREAMING_SNAKE_CASE_   :	int													=   [''] * batch_size
										elif type(UpperCamelCase__       ) is not type(UpperCamelCase__       ):
														raise TypeError(
														    F'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__       )} !='''
														    F''' {type(UpperCamelCase__       )}.'''       )
										elif isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [negative_prompt]
										elif batch_size != len(UpperCamelCase__       ):
														raise ValueError(
														    F'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__       )}, but `prompt`:'''
														    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
														    ' the batch size of `prompt`.'       )
										else:
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   negative_prompt

										SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer(
										    UpperCamelCase__ ,    padding='max_length' ,    max_length=77 ,    truncation=UpperCamelCase__ ,    return_attention_mask=UpperCamelCase__ ,    add_special_tokens=UpperCamelCase__ ,    return_tensors='pt' ,    )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   uncond_input.input_ids.to(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   uncond_input.attention_mask.to(UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.text_encoder(
										    input_ids=UpperCamelCase__ ,    attention_mask=UpperCamelCase__       )

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   negative_prompt_embeds.shape[1]
										SCREAMING_SNAKE_CASE_   :	str													=   negative_prompt_embeds.repeat(1 ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	str													=   negative_prompt_embeds.view(batch_size * num_images_per_prompt ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Any													=   uncond_text_encoder_hidden_states.shape[1]
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   uncond_text_encoder_hidden_states.repeat(1 ,    UpperCamelCase__ ,    1       )
										SCREAMING_SNAKE_CASE_   :	int													=   uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt ,    UpperCamelCase__ ,    -1       )
										SCREAMING_SNAKE_CASE_   :	str													=   uncond_text_mask.repeat_interleave(UpperCamelCase__ ,    dim=0       )

										# done duplicates

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										SCREAMING_SNAKE_CASE_   :	int													=   torch.cat([negative_prompt_embeds, prompt_embeds]       )
										SCREAMING_SNAKE_CASE_   :	Any													=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

										SCREAMING_SNAKE_CASE_   :	str													=   torch.cat([uncond_text_mask, text_mask]       )

						return prompt_embeds, text_encoder_hidden_states, text_mask


		def   __snake_case			(						self ,    UpperCamelCase__=0       ):
						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError('Please install accelerate via `pip install accelerate`'       )

						SCREAMING_SNAKE_CASE_   :	Any													=   torch.device(F'''cuda:{gpu_id}'''       )

						SCREAMING_SNAKE_CASE_   :	Any													=   [
						    self.unet,
						    self.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
														cpu_offload(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__=0       ):
						if is_accelerate_available() and is_accelerate_version('>=' ,    '0.17.0.dev0'       ):
										from accelerate import cpu_offload_with_hook
						else:
										raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.device(F'''cuda:{gpu_id}'''       )

						if self.device.type != "cpu":
										self.to('cpu' ,    silence_dtype_warnings=UpperCamelCase__       )
										torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   None
						for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   cpu_offload_with_hook(UpperCamelCase__ ,    UpperCamelCase__ ,    prev_module_hook=UpperCamelCase__       )

						if self.safety_checker is not None:
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   cpu_offload_with_hook(self.safety_checker ,    UpperCamelCase__ ,    prev_module_hook=UpperCamelCase__       )

						# We'll offload the last model manually.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   hook


		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def   __snake_case			(						self       ):
						if not hasattr(self.unet ,    '_hf_hook'       ):
										return self.device
						for module in self.unet.modules():
										if (
										    hasattr(UpperCamelCase__ ,    '_hf_hook'       )
										    and hasattr(module._hf_hook ,    'execution_device'       )
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device       )
						return self.device






		@torch.no_grad()
		@replace_example_docstring(UpperCamelCase__       )
		def __call__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 512 ,    UpperCamelCase__ = 100 ,    UpperCamelCase__ = 4.0 ,    UpperCamelCase__ = 1 ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "pil" ,    UpperCamelCase__ = True ,    ):

						if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   1
						elif isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   len(UpperCamelCase__       )
						else:
										raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__       )}'''       )

						SCREAMING_SNAKE_CASE_   :	str													=   self._execution_device

						SCREAMING_SNAKE_CASE_   :	str													=   batch_size * num_images_per_prompt
						SCREAMING_SNAKE_CASE_   :	int													=   guidance_scale > 1.0

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   self._encode_prompt(
						    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )

						if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(UpperCamelCase__ ,    dim=0       )
						if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.cat(UpperCamelCase__ ,    dim=0       )

						if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE_   :	Dict													=   image_embeds.repeat_interleave(UpperCamelCase__ ,    dim=0       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   negative_image_embeds.repeat_interleave(UpperCamelCase__ ,    dim=0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.cat([negative_image_embeds, image_embeds] ,    dim=0       ).to(
						    dtype=prompt_embeds.dtype ,    device=UpperCamelCase__       )

						self.scheduler.set_timesteps(UpperCamelCase__ ,    device=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.scheduler.timesteps

						SCREAMING_SNAKE_CASE_   :	Any													=   self.unet.config.in_channels

						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_new_h_w(UpperCamelCase__ ,    UpperCamelCase__ ,    self.movq_scale_factor       )

						# create initial latent
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.prepare_latents(
						    (batch_size, num_channels_latents, height, width) ,    text_encoder_hidden_states.dtype ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    self.scheduler ,    )

						for i, t in enumerate(self.progress_bar(UpperCamelCase__       )       ):
										# expand the latents if we are doing classifier free guidance
										SCREAMING_SNAKE_CASE_   :	Any													=   torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

										SCREAMING_SNAKE_CASE_   :	List[str]													=   {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
										SCREAMING_SNAKE_CASE_   :	str													=   self.unet(
										    sample=UpperCamelCase__ ,    timestep=UpperCamelCase__ ,    encoder_hidden_states=UpperCamelCase__ ,    added_cond_kwargs=UpperCamelCase__ ,    return_dict=UpperCamelCase__ ,    )[0]

										if do_classifier_free_guidance:
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   noise_pred.split(latents.shape[1] ,    dim=1       )
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   noise_pred.chunk(2       )
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   variance_pred.chunk(2       )
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.cat([noise_pred, variance_pred_text] ,    dim=1       )

										if not (
										    hasattr(self.scheduler.config ,    'variance_type'       )
										    and self.scheduler.config.variance_type in ["learned", "learned_range"]
										):
														SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   noise_pred.split(latents.shape[1] ,    dim=1       )

										# compute the previous noisy sample x_t -> x_t-1
										SCREAMING_SNAKE_CASE_   :	Any													=   self.scheduler.step(
										    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    generator=UpperCamelCase__ ,    ).prev_sample
						# post-processing
						SCREAMING_SNAKE_CASE_   :	int													=   self.movq.decode(UpperCamelCase__ ,    force_not_quantize=UpperCamelCase__       )['sample']

						if output_type not in ["pt", "np", "pil"]:
										raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

						if output_type in ["np", "pil"]:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image * 0.5 + 0.5
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image.clamp(0 ,    1       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   image.cpu().permute(0 ,    2 ,    3 ,    1       ).float().numpy()

						if output_type == "pil":
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.numpy_to_pil(UpperCamelCase__       )

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=UpperCamelCase__       )







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 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	int													=   start_index
				while not lines[end_index].startswith(lowerCAmelCase__      ):
								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 					_lowerCamelCase(			lowerCAmelCase__				:		Dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   TASK_GUIDE_TO_MODELS[task_guide]
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase__    ,    set()      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {
				    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 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	Dict													=   get_model_list_for_task(lowerCAmelCase__      )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )    ,    '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)