

"""simple docstring"""


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__    (    __SCREAMING_SNAKE_CASE )	->	List[Any]:
		__lowerCAmelCase:       str          =			[
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "_float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       int          =			dct.pop(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Any          =			val






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Tuple:
		__lowerCAmelCase:       Optional[Any]          =			torch.load(__SCREAMING_SNAKE_CASE					, map_location="cpu" )
		__lowerCAmelCase:       Dict          =			torch.hub.load("pytorch/fairseq"					, "bart.large.cnn" ).eval()
		hub_interface.model.load_state_dict(sd["model"] )
		return hub_interface






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		__lowerCAmelCase      , __lowerCAmelCase:       Any          =			emb.weight.shape
		__lowerCAmelCase:       Optional[int]          =			nn.Linear(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, bias=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       str          =			emb.weight.data
		return lin_layer






@torch.no_grad()
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=None )	->	Tuple:
		if not os.path.exists(__SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       int          =			torch.hub.load("pytorch/fairseq"					, __SCREAMING_SNAKE_CASE ).eval()
		else:
				__lowerCAmelCase:       int          =			load_xsum_checkpoint(__SCREAMING_SNAKE_CASE )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				__lowerCAmelCase:       str          =			checkpoint_path.replace("."					, "-" )
		__lowerCAmelCase:       Optional[Any]          =			BartConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Tuple          =			bart.encode(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
		__lowerCAmelCase:       Tuple          =			BartTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ).encode(__SCREAMING_SNAKE_CASE					, return_tensors="pt" ).unsqueeze(0 )
		if not torch.eq(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ).all():
				raise ValueError(
				    F"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}" )

		if checkpoint_path == "bart.large.mnli":
				__lowerCAmelCase:       Tuple          =			bart.state_dict()
				remove_ignore_keys_(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Any          =			state_dict["model.decoder.embed_tokens.weight"]
				for src, dest in mnli_rename_keys:
						rename_key(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       List[Any]          =			BartForSequenceClassification(__SCREAMING_SNAKE_CASE ).eval()
				model.load_state_dict(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Optional[Any]          =			bart.predict("mnli"					, __SCREAMING_SNAKE_CASE					, return_logits=__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Union[str, Any]          =			model(__SCREAMING_SNAKE_CASE )[0]  # logits
		else:  # no classification heads to worry about
				__lowerCAmelCase:       Dict          =			bart.model.state_dict()
				remove_ignore_keys_(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       Tuple          =			state_dict["decoder.embed_tokens.weight"]
				__lowerCAmelCase:       Optional[Any]          =			bart.extract_features(__SCREAMING_SNAKE_CASE )
				if hf_checkpoint_name == "facebook/bart-large":
						__lowerCAmelCase:       Union[str, Any]          =			BartModel(__SCREAMING_SNAKE_CASE ).eval()
						model.load_state_dict(__SCREAMING_SNAKE_CASE )
						__lowerCAmelCase:       Any          =			model(__SCREAMING_SNAKE_CASE ).model[0]
				else:
						__lowerCAmelCase:       Any          =			BartForConditionalGeneration(__SCREAMING_SNAKE_CASE ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(__SCREAMING_SNAKE_CASE )
						if hasattr(__SCREAMING_SNAKE_CASE					, "lm_head" ):
								__lowerCAmelCase:       Any          =			make_linear_from_emb(model.model.shared )
						__lowerCAmelCase:       Union[str, Any]          =			model.model(__SCREAMING_SNAKE_CASE )[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(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
		model.save_pretrained(__SCREAMING_SNAKE_CASE )


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)





"""simple docstring"""


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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


__A						   =			logging.get_logger(__name__)





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  ["""pixel_values"""]

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Dict[str, int]] = None     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			size if size is not None else {"shortest_edge": 2_5_6}
						__lowerCAmelCase:       str          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:       Optional[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       str          =			do_resize
						__lowerCAmelCase:       Any          =			size
						__lowerCAmelCase:       Dict          =			resample
						__lowerCAmelCase:       Tuple          =			do_center_crop
						__lowerCAmelCase:       str          =			crop_size
						__lowerCAmelCase:       List[Any]          =			do_rescale
						__lowerCAmelCase:       int          =			rescale_factor
						__lowerCAmelCase:       List[Any]          =			do_normalize
						__lowerCAmelCase:       Any          =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase:       Optional[Any]          =			image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Dict     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						if "shortest_edge" not in size:
								raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
						__lowerCAmelCase:       Optional[Any]          =			get_resize_output_image_size(UpperCamelCase__     ,  size=size["shortest_edge"]     ,  default_to_square=UpperCamelCase__)
						return resize(UpperCamelCase__     ,  size=UpperCamelCase__     ,  resample=UpperCamelCase__     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Tuple     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			get_size_dict(UpperCamelCase__)
						if "height" not in size or "width" not in size:
								raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(UpperCamelCase__     ,  size=(size["height"], size["width"])     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				float     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[int])->   np.ndarray:



						'''simple docstring'''


						return rescale(UpperCamelCase__     ,  scale=UpperCamelCase__     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   np.ndarray:



						'''simple docstring'''


						return normalize(UpperCamelCase__     ,  mean=UpperCamelCase__     ,  std=UpperCamelCase__     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				ImageInput     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				PILImageResampling = None     ,  UpperCamelCase__							:				bool = None     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[float] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[str, TensorType]] = None     ,  UpperCamelCase__							:				Union[str, ChannelDimension] = ChannelDimension.FIRST     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase:       str          =			size if size is not None else self.size
						__lowerCAmelCase:       Tuple          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			resample if resample is not None else self.resample
						__lowerCAmelCase:       str          =			do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase:       Tuple          =			crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase:       List[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       List[Any]          =			do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase:       Optional[int]          =			rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase:       Optional[int]          =			do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase:       Union[str, Any]          =			image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase:       Tuple          =			image_std if image_std is not None else self.image_std

						__lowerCAmelCase:       Union[str, Any]          =			make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								raise ValueError(
								    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
								    "torch.Tensor, tf.Tensor or jax.ndarray.")

						if do_resize and size is None:
								raise ValueError("Size must be specified if do_resize is True.")

						if do_center_crop and crop_size is None:
								raise ValueError("Crop size must be specified if do_center_crop is True.")

						if do_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True.")

						if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("Image mean and std must be specified if do_normalize is True.")

						# All transformations expect numpy arrays.
						__lowerCAmelCase:       Tuple          =			[to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase:       Union[str, Any]          =			[self.resize(image=UpperCamelCase__     ,  size=UpperCamelCase__     ,  resample=UpperCamelCase__) for image in images]

						if do_center_crop:
								__lowerCAmelCase:       Optional[Any]          =			[self.center_crop(image=UpperCamelCase__     ,  size=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase:       Optional[Any]          =			[self.rescale(image=UpperCamelCase__     ,  scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase:       List[str]          =			[self.normalize(image=UpperCamelCase__     ,  mean=UpperCamelCase__     ,  std=UpperCamelCase__) for image in images]

						__lowerCAmelCase:       Optional[Any]          =			[to_channel_dimension_format(UpperCamelCase__     ,  UpperCamelCase__) for image in images]

						__lowerCAmelCase:       List[str]          =			{"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__     ,  tensor_type=UpperCamelCase__)




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Tuple] = None)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										raise ValueError(
										    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

								if is_torch_tensor(UpperCamelCase__):
										__lowerCAmelCase:       Optional[int]          =			target_sizes.numpy()

								__lowerCAmelCase:       List[Any]          =			[]

								for idx in range(len(UpperCamelCase__)):
										__lowerCAmelCase:       List[str]          =			torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0)     ,  size=target_sizes[idx]     ,  mode="bilinear"     ,  align_corners=UpperCamelCase__)
										__lowerCAmelCase:       Optional[Any]          =			resized_logits[0].argmax(dim=0)
										semantic_segmentation.append(UpperCamelCase__)
						else:
								__lowerCAmelCase:       Tuple          =			logits.argmax(dim=1)
								__lowerCAmelCase:       Optional[Any]          =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation


