



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


__A       =						['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
__A       =						{'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()
__A       =						logging.get_logger(__name__)

__A       =						' Hello world! cécé herlolip'

__A       =						[
    ('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_       : List[str]    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """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_       : List[Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Tuple    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       dct.pop(lowerCamelCase_    )
						UpperCAmelCase_=       val


def 	__a					(   lowerCAmelCase_       : int    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCamelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       torch.hub.load("""pytorch/fairseq"""      ,"""bart.large.cnn"""    ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]    )
						return hub_interface


def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCamelCase_      ,lowerCamelCase_      ,bias=lowerCamelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer


@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Optional[int]:






						'''simple docstring'''



						if not os.path.exists(lowerCamelCase_    ):
												UpperCAmelCase_=       torch.hub.load("""pytorch/fairseq"""      ,lowerCamelCase_    ).eval()
						else:
												UpperCAmelCase_=       load_xsum_checkpoint(lowerCamelCase_    )

						bart.model.upgrade_state_dict(bart.model.state_dict()    )
						if hf_checkpoint_name is None:
												UpperCAmelCase_=       checkpoint_path.replace("""."""      ,"""-"""    )
						UpperCAmelCase_=       BartConfig.from_pretrained(lowerCamelCase_    )
						UpperCAmelCase_=       bart.encode(lowerCamelCase_    ).unsqueeze(0    )
						UpperCAmelCase_=       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":
												UpperCAmelCase_=       bart.state_dict()
												remove_ignore_keys_(lowerCamelCase_    )
												UpperCAmelCase_=       state_dict["""model.decoder.embed_tokens.weight"""]
												for src, dest in mnli_rename_keys:
																		rename_key(lowerCamelCase_      ,lowerCamelCase_      ,lowerCamelCase_    )
												UpperCAmelCase_=       BartForSequenceClassification(lowerCamelCase_    ).eval()
												model.load_state_dict(lowerCamelCase_    )
												UpperCAmelCase_=       bart.predict("""mnli"""      ,lowerCamelCase_      ,return_logits=lowerCamelCase_    )
												UpperCAmelCase_=       model(lowerCamelCase_    )[0]  # logits
						else:  # no classification heads to worry about
												UpperCAmelCase_=       bart.model.state_dict()
												remove_ignore_keys_(lowerCamelCase_    )
												UpperCAmelCase_=       state_dict["""decoder.embed_tokens.weight"""]
												UpperCAmelCase_=       bart.extract_features(lowerCamelCase_    )
												if hf_checkpoint_name == "facebook/bart-large":
																		UpperCAmelCase_=       BartModel(lowerCamelCase_    ).eval()
																		model.load_state_dict(lowerCamelCase_    )
																		UpperCAmelCase_=       model(lowerCamelCase_    ).model[0]
												else:
																		UpperCAmelCase_=       BartForConditionalGeneration(lowerCamelCase_    ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(lowerCamelCase_    )
																		if hasattr(lowerCamelCase_      ,"""lm_head"""    ):
																								UpperCAmelCase_=       make_linear_from_emb(model.model.shared    )
																		UpperCAmelCase_=       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__":
	__A       =						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'''
	)
	__A       =						parser.parse_args()
	convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




