






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class      UpperCamelCase (				UpperCAmelCase_ ):







		"""simple docstring"""



		snake_case    =   42
		snake_case    =   42
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Any:


						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=_lowercase						,scheduler=_lowercase		)







		@torch.no_grad()
		def __call__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Tuple = 1						,_SCREAMING_SNAKE_CASE		:       int = 5_0						,_SCREAMING_SNAKE_CASE		:       Dict = None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any] = "pil"						,_SCREAMING_SNAKE_CASE		:       int = True						,**_SCREAMING_SNAKE_CASE		:       int						,)    ->   Union[Tuple, ImagePipelineOutput]:


						'''simple docstring'''
						A	 =      self.unet.config.sample_size
						A	 =      (batch_size, 3, img_size, img_size)

						A	 =      self.unet

						# sample x_0 ~ N(0, sigma_0^2 * I)
						A	 =      randn_tensor(_lowercase						,generator=_lowercase						,device=self.device		) * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(_lowercase		)

						for t in self.progress_bar(self.scheduler.timesteps		):
										# here sigma_t == t_i from the paper
										A	 =      self.scheduler.schedule[t]
										A	 =      self.scheduler.schedule[t - 1] if t > 0 else 0

										# 1. Select temporarily increased noise level sigma_hat
										# 2. Add new noise to move from sample_i to sample_hat
										A	 =      self.scheduler.add_noise_to_input(_lowercase						,_lowercase						,generator=_lowercase		)

										# 3. Predict the noise residual given the noise magnitude `sigma_hat`
										# The model inputs and output are adjusted by following eq. (213) in [1].
										A	 =      (sigma_hat / 2) * model((sample_hat + 1) / 2						,sigma_hat / 2		).sample

										# 4. Evaluate dx/dt at sigma_hat
										# 5. Take Euler step from sigma to sigma_prev
										A	 =      self.scheduler.step(_lowercase						,_lowercase						,_lowercase						,_lowercase		)

										if sigma_prev != 0:
														# 6. Apply 2nd order correction
														# The model inputs and output are adjusted by following eq. (213) in [1].
														A	 =      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,sigma_prev / 2		).sample
														A	 =      self.scheduler.step_correct(
														    _lowercase						,_lowercase						,_lowercase						,_lowercase						,step_output.prev_sample						,step_output['derivative']						,)
										A	 =      step_output.prev_sample

						A	 =      (sample / 2 + 0.5).clamp(0						,1		)
						A	 =      sample.cpu().permute(0						,2						,3						,1		).numpy()
						if output_type == "pil":
										A	 =      self.numpy_to_pil(_lowercase		)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=_lowercase		)













import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				A	 =      [
				    'encoder.version',
				    'decoder.version',
				    'model.encoder.version',
				    'model.decoder.version',
				    'decoder.output_projection.weight',
				    '_float_tensor',
				    'encoder.embed_positions._float_tensor',
				    'decoder.embed_positions._float_tensor',
				]
				for k in ignore_keys:
								state_dict.pop(UpperCAmelCase,						UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      list(s_dict.keys()   )
				for key in keys:
								if "transformer_layers" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )
								elif "subsample" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A ,		A	 =      emb.weight.shape
				A	 =      nn.Linear(UpperCAmelCase,						UpperCAmelCase,						bias=UpperCAmelCase   )
				A	 =      emb.weight.data
				return lin_layer







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )
				A	 =      mam_aaa['args']
				A	 =      mam_aaa['model']
				A	 =      state_dict['decoder.output_projection.weight']

				remove_ignore_keys_(UpperCAmelCase   )
				rename_keys(UpperCAmelCase   )

				A	 =      state_dict['decoder.embed_tokens.weight'].shape[0]

				A	 =      args.share_decoder_input_output_embed

				A	 =      [int(UpperCAmelCase   ) for i in args.conv_kernel_sizes.split(','   )]
				A	 =      SpeechaTextConfig(
				    vocab_size=UpperCAmelCase,						max_source_positions=args.max_source_positions,						max_target_positions=args.max_target_positions,						encoder_layers=args.encoder_layers,						decoder_layers=args.decoder_layers,						encoder_attention_heads=args.encoder_attention_heads,						decoder_attention_heads=args.decoder_attention_heads,						encoder_ffn_dim=args.encoder_ffn_embed_dim,						decoder_ffn_dim=args.decoder_ffn_embed_dim,						d_model=args.encoder_embed_dim,						dropout=args.dropout,						attention_dropout=args.attention_dropout,						activation_dropout=args.activation_dropout,						activation_function='relu',						num_conv_layers=len(UpperCAmelCase   ),						conv_channels=args.conv_channels,						conv_kernel_sizes=UpperCAmelCase,						input_feat_per_channel=args.input_feat_per_channel,						input_channels=args.input_channels,						tie_word_embeddings=UpperCAmelCase,						num_beams=5,						max_length=2_00,						use_cache=UpperCAmelCase,						decoder_start_token_id=2,						early_stopping=UpperCAmelCase,						)

				A	 =      SpeechaTextForConditionalGeneration(UpperCAmelCase   )
				A ,		A	 =      model.model.load_state_dict(UpperCAmelCase,						strict=UpperCAmelCase   )
				if len(UpperCAmelCase   ) > 0 and not set(UpperCAmelCase   ) <= {
				    "encoder.embed_positions.weights",
				    "decoder.embed_positions.weights",
				}:
								raise ValueError(
								    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
								    f' but all the following weights are missing {missing}'   )

				if tie_embeds:
								A	 =      make_linear_from_emb(model.model.decoder.embed_tokens   )
				else:
								A	 =      lm_head_weights

				model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					lowerCAmelCase_           = parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





