





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)  # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Optional[int]		,   _A      :      WhisperForConditionalGeneration		,   _A      :      WhisperProcessor		,   _A      :      AutoencoderKL		,   _A      :      CLIPTextModel		,   _A      :      CLIPTokenizer		,   _A      :      UNetaDConditionModel		,   _A      :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,   _A      :      StableDiffusionSafetyChecker		,   _A      :      CLIPImageProcessor		,   )							->    str:







							"""simple docstring"""


							super().__init__()

							if safety_checker is None:
								logger.warning(
								    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
								    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
								    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
								    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
								    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
								    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'	)

							self.register_modules(
							    speech_model=_A		,   speech_processor=_A		,   vae=_A		,   text_encoder=_A		,   tokenizer=_A		,   unet=_A		,   scheduler=_A		,   feature_extractor=_A		,   )






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Optional[Union[str, int]] = "auto"	)							->    int:







							"""simple docstring"""


							if slice_size == "auto":
								snake_case_		:			Dict											=	self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_A	)






						def UpperCAmelCase_				(      self      :      int	)							->    Dict:







							"""simple docstring"""


							self.enable_attention_slicing(_A	)






						@torch.no_grad()
						def __call__(      self      :      List[str]		,   _A      :      Tuple		,   _A      :      List[str]=16000		,   _A      :      int = 512		,   _A      :      int = 512		,   _A      :      int = 50		,   _A      :      float = 7.5		,   _A      :      Optional[Union[str, List[str]]] = None		,   _A      :      Optional[int] = 1		,   _A      :      float = 0.0		,   _A      :      Optional[torch.Generator] = None		,   _A      :      Optional[torch.FloatTensor] = None		,   _A      :      Optional[str] = "pil"		,   _A      :      bool = True		,   _A      :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,   _A      :      int = 1		,   **_A      :      int		,   )							->    str:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.speech_processor.feature_extractor(
							    _A		,   return_tensors='pt'		,   sampling_rate=_A	).input_features.to(self.device	)
							snake_case_		:			Union[str, Any]											=	self.speech_model.generate(_A		,   max_length=480000	)

							snake_case_		:			int											=	self.speech_processor.tokenizer.batch_decode(_A		,   skip_special_tokens=_A		,   normalize=_A	)[
							    0
							]

							if isinstance(_A		,   _A	):
								snake_case_		:			int											=	1
							elif isinstance(_A		,   _A	):
								snake_case_		:			Any											=	len(_A	)
							else:
								raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_A	)}"""	)

							if height % 8 != 0 or width % 8 != 0:
								raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(_A		,   _A	) or callback_steps <= 0)
							):
								raise ValueError(
								    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
								    F""" {type(_A	)}."""	)

							# get prompt text embeddings
							snake_case_		:			int											=	self.tokenizer(
							    _A		,   padding='max_length'		,   max_length=self.tokenizer.model_max_length		,   return_tensors='pt'		,   )
							snake_case_		:			Optional[Any]											=	text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
								snake_case_		:			Optional[int]											=	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
								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}"""	)
								snake_case_		:			int											=	text_input_ids[:, : self.tokenizer.model_max_length]
							snake_case_		:			Tuple											=	self.text_encoder(text_input_ids.to(self.device	)	)[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							snake_case_						,snake_case_						,snake_case_		:			List[Any]											=	text_embeddings.shape
							snake_case_		:			Union[str, Any]											=	text_embeddings.repeat(1		,   _A		,   1	)
							snake_case_		:			Optional[Any]											=	text_embeddings.view(bs_embed * num_images_per_prompt		,   _A		,   -1	)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							snake_case_		:			Optional[Any]											=	guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
								snake_case_		:			List[str]
								if negative_prompt is None:
									snake_case_		:			Dict											=	[''] * batch_size
								elif type(_A	) is not type(_A	):
									raise TypeError(
									    F"""`negative_prompt` should be the same type to `prompt`, but got {type(_A	)} !="""
									    F""" {type(_A	)}."""	)
								elif isinstance(_A		,   _A	):
									snake_case_		:			Tuple											=	[negative_prompt]
								elif batch_size != len(_A	):
									raise ValueError(
									    F"""`negative_prompt`: {negative_prompt} has batch size {len(_A	)}, but `prompt`:"""
									    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
									    ' the batch size of `prompt`.'	)
								else:
									snake_case_		:			Optional[Any]											=	negative_prompt

								snake_case_		:			Any											=	text_input_ids.shape[-1]
								snake_case_		:			Dict											=	self.tokenizer(
								    _A		,   padding='max_length'		,   max_length=_A		,   truncation=_A		,   return_tensors='pt'		,   )
								snake_case_		:			Optional[int]											=	self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

								# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
								snake_case_		:			Dict											=	uncond_embeddings.shape[1]
								snake_case_		:			Dict											=	uncond_embeddings.repeat(1		,   _A		,   1	)
								snake_case_		:			Optional[int]											=	uncond_embeddings.view(batch_size * num_images_per_prompt		,   _A		,   -1	)

								# 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
								snake_case_		:			Optional[int]											=	torch.cat([uncond_embeddings, text_embeddings]	)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							snake_case_		:			Union[str, Any]											=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							snake_case_		:			Tuple											=	text_embeddings.dtype
							if latents is None:
								if self.device.type == "mps":
									# randn does not exist on mps
									snake_case_		:			str											=	torch.randn(_A		,   generator=_A		,   device='cpu'		,   dtype=_A	).to(
									    self.device	)
								else:
									snake_case_		:			int											=	torch.randn(_A		,   generator=_A		,   device=self.device		,   dtype=_A	)
							else:
								if latents.shape != latents_shape:
									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
								snake_case_		:			Optional[Any]											=	latents.to(self.device	)

							# set timesteps
							self.scheduler.set_timesteps(_A	)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							snake_case_		:			Optional[int]											=	self.scheduler.timesteps.to(self.device	)

							# scale the initial noise by the standard deviation required by the scheduler
							snake_case_		:			int											=	latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							snake_case_		:			Union[str, Any]											=	'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
							snake_case_		:			Optional[int]											=	{}
							if accepts_eta:
								snake_case_		:			Tuple											=	eta

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

								# predict the noise residual
								snake_case_		:			str											=	self.unet(_A		,   _A		,   encoder_hidden_states=_A	).sample

								# perform guidance
								if do_classifier_free_guidance:
									snake_case_						,snake_case_		:			Optional[int]											=	noise_pred.chunk(2	)
									snake_case_		:			Optional[int]											=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								snake_case_		:			Any											=	self.scheduler.step(_A		,   _A		,   _A		,   **_A	).prev_sample

								# call the callback, if provided
								if callback is not None and i % callback_steps == 0:
									callback(_A		,   _A		,   _A	)

							snake_case_		:			Union[str, Any]											=	1 / 0.1_8_2_1_5 * latents
							snake_case_		:			int											=	self.vae.decode(_A	).sample

							snake_case_		:			Dict											=	(image / 2 + 0.5).clamp(0		,   1	)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							snake_case_		:			str											=	image.cpu().permute(0		,   2		,   3		,   1	).float().numpy()

							if output_type == "pil":
								snake_case_		:			Optional[int]											=	self.numpy_to_pil(_A	)

							if not return_dict:
								return image

							return StableDiffusionPipelineOutput(images=_A		,   nsfw_content_detected=_A	)








import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		if not is_key_init:
			raise ValueError(f"""{key} was not correctly initialized!"""   )

	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	prophet.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

