





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

__A				:    Dict       =   ''' Hello world! cécé herlolip'''

__A				:    Tuple       =   [
    ('''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				[
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase			,			__lowercase )





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				Any:







			'''simple docstring'''

			UpperCAmelCase   		=				dct.pop(__lowercase )
			UpperCAmelCase   		=				val





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(__lowercase			,			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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase		,			UpperCAmelCase   		=				emb.weight.shape
			UpperCAmelCase   		=				nn.Linear(__lowercase			,			__lowercase			,			bias=__lowercase )
			UpperCAmelCase   		=				emb.weight.data
			return lin_layer





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None )  ->				Tuple:







			'''simple docstring'''

			if not os.path.exists(__lowercase ):
						UpperCAmelCase   		=				torch.hub.load('''pytorch/fairseq'''			,			__lowercase ).eval()
			else:
						UpperCAmelCase   		=				load_xsum_checkpoint(__lowercase )

			bart.model.upgrade_state_dict(bart.model.state_dict() )
			if hf_checkpoint_name is None:
						UpperCAmelCase   		=				checkpoint_path.replace('''.'''			,			'''-''' )
			UpperCAmelCase   		=				BartConfig.from_pretrained(__lowercase )
			UpperCAmelCase   		=				bart.encode(__lowercase ).unsqueeze(0 )
			UpperCAmelCase   		=				BartTokenizer.from_pretrained(__lowercase ).encode(__lowercase			,			return_tensors='''pt''' ).unsqueeze(0 )
			if not torch.eq(__lowercase			,			__lowercase ).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_(__lowercase )
						UpperCAmelCase   		=				state_dict['''model.decoder.embed_tokens.weight''']
						for src, dest in mnli_rename_keys:
									rename_key(__lowercase			,			__lowercase			,			__lowercase )
						UpperCAmelCase   		=				BartForSequenceClassification(__lowercase ).eval()
						model.load_state_dict(__lowercase )
						UpperCAmelCase   		=				bart.predict('''mnli'''			,			__lowercase			,			return_logits=__lowercase )
						UpperCAmelCase   		=				model(__lowercase )[0]  # logits
			else:  # no classification heads to worry about
						UpperCAmelCase   		=				bart.model.state_dict()
						remove_ignore_keys_(__lowercase )
						UpperCAmelCase   		=				state_dict['''decoder.embed_tokens.weight''']
						UpperCAmelCase   		=				bart.extract_features(__lowercase )
						if hf_checkpoint_name == "facebook/bart-large":
									UpperCAmelCase   		=				BartModel(__lowercase ).eval()
									model.load_state_dict(__lowercase )
									UpperCAmelCase   		=				model(__lowercase ).model[0]
						else:
									UpperCAmelCase   		=				BartForConditionalGeneration(__lowercase ).eval()  # an existing summarization ckpt
									model.model.load_state_dict(__lowercase )
									if hasattr(__lowercase			,			'''lm_head''' ):
												UpperCAmelCase   		=				make_linear_from_emb(model.model.shared )
									UpperCAmelCase   		=				model.model(__lowercase )[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(__lowercase ).mkdir(exist_ok=__lowercase )
			model.save_pretrained(__lowercase )


if __name__ == "__main__":
				__A				:    Optional[Any]       =   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				:    Union[str, Any]       =   parser.parse_args()
				convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)










'''simple docstring'''




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase							,  _UpperCAmelCase     							=   image.size
     _UpperCAmelCase							,  _UpperCAmelCase     							=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     _UpperCAmelCase     							=   image.resize((w, h)			,     resample=PIL_INTERPOLATION["lanczos"]       )
     _UpperCAmelCase     							=   np.array(__lowercase       ).astype(np.floataa       ) / 255.0
     _UpperCAmelCase     							=   image[None].transpose(0			,     3			,     1			,     2       )
     _UpperCAmelCase     							=   torch.from_numpy(__lowercase       )
     return 2.0 * image - 1.0







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    VQModel	,			snake_case_       :    UNetaDModel	,			snake_case_       :    Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]	,			):
      super().__init__()
      self.register_modules(vqvae=snake_case_	,			unet=snake_case_	,			scheduler=snake_case_    )




 @torch.no_grad()
 def __call__(					self       :    Any	,			snake_case_       :    Union[torch.Tensor, PIL.Image.Image] = None	,			snake_case_       :    Optional[int] = 1	,			snake_case_       :    Optional[int] = 1_0_0	,			snake_case_       :    Optional[float] = 0.0	,			snake_case_       :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			snake_case_       :    Optional[str] = "pil"	,			snake_case_       :    bool = True	,			):
      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   1
      elif isinstance(snake_case_	,			torch.Tensor    ):
           _UpperCAmelCase     							=   image.shape[0]
      else:
           raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_    )}'    )

      if isinstance(snake_case_	,			PIL.Image.Image    ):
           _UpperCAmelCase     							=   preprocess(snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _UpperCAmelCase     							=   (batch_size, self.unet.config.in_channels // 2, height, width)
      _UpperCAmelCase     							=   next(self.unet.parameters()    ).dtype

      _UpperCAmelCase     							=   randn_tensor(snake_case_	,			generator=snake_case_	,			device=self.device	,			dtype=snake_case_    )

      _UpperCAmelCase     							=   image.to(device=self.device	,			dtype=snake_case_    )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(snake_case_	,			device=self.device    )
      _UpperCAmelCase     							=   self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase     							=   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]
      _UpperCAmelCase     							=   "eta" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      _UpperCAmelCase     							=   {}
      if accepts_eta:
           _UpperCAmelCase     							=   eta

      for t in self.progress_bar(snake_case_    ):
           # concat latents and low resolution image in the channel dimension.
           _UpperCAmelCase     							=   torch.cat([latents, image]	,			dim=1    )
           _UpperCAmelCase     							=   self.scheduler.scale_model_input(snake_case_	,			snake_case_    )
           # predict the noise residual
           _UpperCAmelCase     							=   self.unet(snake_case_	,			snake_case_    ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _UpperCAmelCase     							=   self.scheduler.step(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    ).prev_sample

      # decode the image latents with the VQVAE
      _UpperCAmelCase     							=   self.vqvae.decode(snake_case_    ).sample
      _UpperCAmelCase     							=   torch.clamp(snake_case_	,			-1.0	,			1.0    )
      _UpperCAmelCase     							=   image / 2 + 0.5
      _UpperCAmelCase     							=   image.cpu().permute(0	,			2	,			3	,			1    ).numpy()

      if output_type == "pil":
           _UpperCAmelCase     							=   self.numpy_to_pil(snake_case_    )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=snake_case_    )




