






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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	42
				a__       							=	42







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__()
								self.register_modules(unet=__lowerCAmelCase							,       scheduler=__lowerCAmelCase  )







				@torch.no_grad()
				def __call__(    self							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 5_0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = "pil"							,       __lowerCAmelCase = True							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.unet.config.sample_size
								__magic_name__		:Tuple   =   (batch_size, 3, img_size, img_size)

								__magic_name__		:str   =   self.unet

								# sample x_0 ~ N(0, sigma_0^2 * I)
								__magic_name__		:Optional[int]   =   randn_tensor(__lowerCAmelCase							,       generator=__lowerCAmelCase							,       device=self.device  ) * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(__lowerCAmelCase  )

								for t in self.progress_bar(self.scheduler.timesteps  ):
												# here sigma_t == t_i from the paper
												__magic_name__		:Tuple   =   self.scheduler.schedule[t]
												__magic_name__		:int   =   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
												__magic_name__  ,					__magic_name__		:Dict   =   self.scheduler.add_noise_to_input(__lowerCAmelCase							,       __lowerCAmelCase							,       generator=__lowerCAmelCase  )

												# 3. Predict the noise residual given the noise magnitude `sigma_hat`
												# The model inputs and output are adjusted by following eq. (213) in [1].
												__magic_name__		:int   =   (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
												__magic_name__		:List[str]   =   self.scheduler.step(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

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

								__magic_name__		:Dict   =   (sample / 2 + 0.5).clamp(0							,       1  )
								__magic_name__		:str   =   sample.cpu().permute(0							,       2							,       3							,       1  ).numpy()
								if output_type == "pil":
												__magic_name__		:List[Any]   =   self.numpy_to_pil(__lowerCAmelCase  )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=__lowerCAmelCase  )












import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)