


'''simple docstring'''






from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


__UpperCAmelCase       							=				logging.get_logger(__name__)


# General docstring
__UpperCAmelCase       							=				'''MobileNetV1Config'''

# Base docstring
__UpperCAmelCase       							=				'''google/mobilenet_v1_1.0_224'''
__UpperCAmelCase       							=				[1, 1_024, 7, 7]

# Image classification docstring
__UpperCAmelCase       							=				'''google/mobilenet_v1_1.0_224'''
__UpperCAmelCase       							=				'''tabby, tabby cat'''


__UpperCAmelCase       							=				[
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]


def    _snake_case  (	A					,       A					,       A=None			)		->       Optional[int]:
							lowerCAmelCase__			  =      {}

							if isinstance(A					,       A			):
														lowerCAmelCase__			  =      model.mobilenet_va
							else:
														lowerCAmelCase__			  =      model

							lowerCAmelCase__			  =      '''MobilenetV1/Conv2d_0/'''
							lowerCAmelCase__			  =      backbone.conv_stem.convolution.weight
							lowerCAmelCase__			  =      backbone.conv_stem.normalization.bias
							lowerCAmelCase__			  =      backbone.conv_stem.normalization.weight
							lowerCAmelCase__			  =      backbone.conv_stem.normalization.running_mean
							lowerCAmelCase__			  =      backbone.conv_stem.normalization.running_var

							for i in range(13			):
														lowerCAmelCase__			  =      i + 1
														lowerCAmelCase__			  =      i * 2

														lowerCAmelCase__			  =      backbone.layer[pt_index]
														lowerCAmelCase__			  =      F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
														lowerCAmelCase__			  =      pointer.convolution.weight
														lowerCAmelCase__			  =      pointer.normalization.bias
														lowerCAmelCase__			  =      pointer.normalization.weight
														lowerCAmelCase__			  =      pointer.normalization.running_mean
														lowerCAmelCase__			  =      pointer.normalization.running_var

														lowerCAmelCase__			  =      backbone.layer[pt_index + 1]
														lowerCAmelCase__			  =      F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
														lowerCAmelCase__			  =      pointer.convolution.weight
														lowerCAmelCase__			  =      pointer.normalization.bias
														lowerCAmelCase__			  =      pointer.normalization.weight
														lowerCAmelCase__			  =      pointer.normalization.running_mean
														lowerCAmelCase__			  =      pointer.normalization.running_var

							if isinstance(A					,       A			):
														lowerCAmelCase__			  =      '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
														lowerCAmelCase__			  =      model.classifier.weight
														lowerCAmelCase__			  =      model.classifier.bias

							return tf_to_pt_map


def    _snake_case  (	A					,       A					,       A			)		->       Any:
							try:
														import numpy as np
														import tensorflow as tf
							except ImportError:
														logger.error(
														    '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
														    '''https://www.tensorflow.org/install/ for installation instructions.'''			)
														raise

							# Load weights from TF model
							lowerCAmelCase__			  =      tf.train.list_variables(A			)
							lowerCAmelCase__			  =      {}
							for name, shape in init_vars:
														logger.info(F"""Loading TF weight {name} with shape {shape}"""			)
														lowerCAmelCase__			  =      tf.train.load_variable(A					,       A			)
														lowerCAmelCase__			  =      array

							# Build TF to PyTorch weights loading map
							lowerCAmelCase__			  =      _build_tf_to_pytorch_map(A					,       A					,       A			)

							for name, pointer in tf_to_pt_map.items():
														logger.info(F"""Importing {name}"""			)
														if name not in tf_weights:
																					logger.info(F"""{name} not in tf pre-trained weights, skipping"""			)
																					continue

														lowerCAmelCase__			  =      tf_weights[name]

														if "depthwise_weights" in name:
																					logger.info('''Transposing depthwise'''			)
																					lowerCAmelCase__			  =      np.transpose(A					,       (2, 3, 0, 1)			)
														elif "weights" in name:
																					logger.info('''Transposing'''			)
																					if len(pointer.shape			) == 2:  # copying into linear layer
																												lowerCAmelCase__			  =      array.squeeze().transpose()
																					else:
																												lowerCAmelCase__			  =      np.transpose(A					,       (3, 2, 0, 1)			)

														if pointer.shape != array.shape:
																					raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""			)

														logger.info(F"""Initialize PyTorch weight {name} {array.shape}"""			)
														lowerCAmelCase__			  =      torch.from_numpy(A			)

														tf_weights.pop(A					,       A			)
														tf_weights.pop(name + '''/RMSProp'''					,       A			)
														tf_weights.pop(name + '''/RMSProp_1'''					,       A			)
														tf_weights.pop(name + '''/ExponentialMovingAverage'''					,       A			)

							logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys()			)}"""			)
							return model


def    _snake_case  (	A					,       A			)		->       torch.Tensor:
							lowerCAmelCase__  ,   lowerCAmelCase__			  =      features.shape[-2:]
							lowerCAmelCase__  ,   lowerCAmelCase__			  =      conv_layer.stride
							lowerCAmelCase__  ,   lowerCAmelCase__			  =      conv_layer.kernel_size

							if in_height % stride_height == 0:
														lowerCAmelCase__			  =      max(kernel_height - stride_height					,       0			)
							else:
														lowerCAmelCase__			  =      max(kernel_height - (in_height % stride_height)					,       0			)

							if in_width % stride_width == 0:
														lowerCAmelCase__			  =      max(kernel_width - stride_width					,       0			)
							else:
														lowerCAmelCase__			  =      max(kernel_width - (in_width % stride_width)					,       0			)

							lowerCAmelCase__			  =      pad_along_width // 2
							lowerCAmelCase__			  =      pad_along_width - pad_left
							lowerCAmelCase__			  =      pad_along_height // 2
							lowerCAmelCase__			  =      pad_along_height - pad_top

							lowerCAmelCase__			  =      (pad_left, pad_right, pad_top, pad_bottom)
							return nn.functional.pad(A					,       A					,       '''constant'''					,       0.0			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_ = 1   ,       lowerCamelCase_ = 1   ,       lowerCamelCase_ = False   ,       lowerCamelCase_ = True   ,       lowerCamelCase_ = True   ,       )			->				None:
														super().__init__()
														lowerCAmelCase__			  =      config

														if in_channels % groups != 0:
																					raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups."""  )
														if out_channels % groups != 0:
																					raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups."""  )

														lowerCAmelCase__			  =      0 if config.tf_padding else int((kernel_size - 1) / 2  )

														lowerCAmelCase__			  =      nn.Convad(
														    in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       kernel_size=lowerCamelCase_   ,       stride=lowerCamelCase_   ,       padding=lowerCamelCase_   ,       groups=lowerCamelCase_   ,       bias=lowerCamelCase_   ,       padding_mode='''zeros'''   ,       )

														if use_normalization:
																					lowerCAmelCase__			  =      nn.BatchNormad(
																					    num_features=lowerCamelCase_   ,       eps=config.layer_norm_eps   ,       momentum=0.9_997   ,       affine=lowerCamelCase_   ,       track_running_stats=lowerCamelCase_   ,       )
														else:
																					lowerCAmelCase__			  =      None

														if use_activation:
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      ACTaFN[use_activation]
																					elif isinstance(config.hidden_act   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      ACTaFN[config.hidden_act]
																					else:
																												lowerCAmelCase__			  =      config.hidden_act
														else:
																					lowerCAmelCase__			  =      None







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				torch.Tensor:
														if self.config.tf_padding:
																					lowerCAmelCase__			  =      apply_tf_padding(lowerCamelCase_   ,       self.convolution  )
														lowerCAmelCase__			  =      self.convolution(lowerCamelCase_  )
														if self.normalization is not None:
																					lowerCAmelCase__			  =      self.normalization(lowerCamelCase_  )
														if self.activation is not None:
																					lowerCAmelCase__			  =      self.activation(lowerCamelCase_  )
														return features




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  int      		= MobileNetVaConfig
							lowercase__     :  Optional[Any]      		= load_tf_weights_in_mobilenet_va
							lowercase__     :  str      		= "mobilenet_v1"
							lowercase__     :  Dict      		= "pixel_values"
							lowercase__     :  Union[str, Any]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				None:
														if isinstance(lowerCamelCase_   ,       (nn.Linear, nn.Convad)  ):
																					module.weight.data.normal_(mean=0.0   ,       std=self.config.initializer_range  )
																					if module.bias is not None:
																												module.bias.data.zero_()
														elif isinstance(lowerCamelCase_   ,       nn.BatchNormad  ):
																					module.bias.data.zero_()
																					module.weight.data.fill_(1.0  )

__UpperCAmelCase       							=				R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__UpperCAmelCase       							=				R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''




@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."							,							a__							,							)
class 						a__							(				a__    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = True  )			->				Dict:
														super().__init__(lowerCamelCase_  )
														lowerCAmelCase__			  =      config

														lowerCAmelCase__			  =      32
														lowerCAmelCase__			  =      max(int(depth * config.depth_multiplier  )   ,       config.min_depth  )

														lowerCAmelCase__			  =      MobileNetVaConvLayer(
														    lowerCamelCase_   ,       in_channels=config.num_channels   ,       out_channels=lowerCamelCase_   ,       kernel_size=3   ,       stride=2   ,       )

														lowerCAmelCase__			  =      [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

														lowerCAmelCase__			  =      nn.ModuleList()
														for i in range(13  ):
																					lowerCAmelCase__			  =      out_channels

																					if strides[i] == 2 or i == 0:
																												depth *= 2
																												lowerCAmelCase__			  =      max(int(depth * config.depth_multiplier  )   ,       config.min_depth  )

																					self.layer.append(
																					    MobileNetVaConvLayer(
																					        lowerCamelCase_   ,       in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       kernel_size=3   ,       stride=strides[i]   ,       groups=lowerCamelCase_   ,       )  )

																					self.layer.append(
																					    MobileNetVaConvLayer(
																					        lowerCamelCase_   ,       in_channels=lowerCamelCase_   ,       out_channels=lowerCamelCase_   ,       kernel_size=1   ,       )  )

														lowerCAmelCase__			  =      nn.AdaptiveAvgPoolad((1, 1)  ) if add_pooling_layer else None

														# Initialize weights and apply final processing
														self.post_init()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Any:
														raise NotImplementedError







							@add_start_docstrings_to_model_forward(lowerCamelCase_  )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC   ,       output_type=lowerCamelCase_   ,       config_class=_CONFIG_FOR_DOC   ,       modality='''vision'''   ,       expected_output=_EXPECTED_OUTPUT_SHAPE   ,       )
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       )			->				Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
														lowerCAmelCase__			  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														lowerCAmelCase__			  =      return_dict if return_dict is not None else self.config.use_return_dict

														if pixel_values is None:
																					raise ValueError('''You have to specify pixel_values'''  )

														lowerCAmelCase__			  =      self.conv_stem(lowerCamelCase_  )

														lowerCAmelCase__			  =      () if output_hidden_states else None

														for i, layer_module in enumerate(self.layer  ):
																					lowerCAmelCase__			  =      layer_module(lowerCamelCase_  )

																					if output_hidden_states:
																												lowerCAmelCase__			  =      all_hidden_states + (hidden_states,)

														lowerCAmelCase__			  =      hidden_states

														if self.pooler is not None:
																					lowerCAmelCase__			  =      torch.flatten(self.pooler(lowerCamelCase_  )   ,       start_dim=1  )
														else:
																					lowerCAmelCase__			  =      None

														if not return_dict:
																					return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None  )

														return BaseModelOutputWithPoolingAndNoAttention(
														    last_hidden_state=lowerCamelCase_   ,       pooler_output=lowerCamelCase_   ,       hidden_states=lowerCamelCase_   ,       )




@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,							a__							,							)
class 						a__							(				a__    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_  )			->				None:
														super().__init__(lowerCamelCase_  )

														lowerCAmelCase__			  =      config.num_labels
														lowerCAmelCase__			  =      MobileNetVaModel(lowerCamelCase_  )

														lowerCAmelCase__			  =      self.mobilenet_va.layer[-1].convolution.out_channels

														# Classifier head
														lowerCAmelCase__			  =      nn.Dropout(config.classifier_dropout_prob   ,       inplace=lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.Linear(lowerCamelCase_   ,       config.num_labels  ) if config.num_labels > 0 else nn.Identity()

														# Initialize weights and apply final processing
														self.post_init()







							@add_start_docstrings_to_model_forward(lowerCamelCase_  )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,       output_type=lowerCamelCase_   ,       config_class=_CONFIG_FOR_DOC   ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,       )
							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       )			->				Union[tuple, ImageClassifierOutputWithNoAttention]:
														lowerCAmelCase__			  =      return_dict if return_dict is not None else self.config.use_return_dict

														lowerCAmelCase__			  =      self.mobilenet_va(lowerCamelCase_   ,       output_hidden_states=lowerCamelCase_   ,       return_dict=lowerCamelCase_  )

														lowerCAmelCase__			  =      outputs.pooler_output if return_dict else outputs[1]

														lowerCAmelCase__			  =      self.classifier(self.dropout(lowerCamelCase_  )  )

														lowerCAmelCase__			  =      None
														if labels is not None:
																					if self.config.problem_type is None:
																												if self.num_labels == 1:
																																			lowerCAmelCase__			  =      '''regression'''
																												elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																			lowerCAmelCase__			  =      '''single_label_classification'''
																												else:
																																			lowerCAmelCase__			  =      '''multi_label_classification'''

																					if self.config.problem_type == "regression":
																												lowerCAmelCase__			  =      MSELoss()
																												if self.num_labels == 1:
																																			lowerCAmelCase__			  =      loss_fct(logits.squeeze()   ,       labels.squeeze()  )
																												else:
																																			lowerCAmelCase__			  =      loss_fct(lowerCamelCase_   ,       lowerCamelCase_  )
																					elif self.config.problem_type == "single_label_classification":
																												lowerCAmelCase__			  =      CrossEntropyLoss()
																												lowerCAmelCase__			  =      loss_fct(logits.view(-1   ,       self.num_labels  )   ,       labels.view(-1  )  )
																					elif self.config.problem_type == "multi_label_classification":
																												lowerCAmelCase__			  =      BCEWithLogitsLoss()
																												lowerCAmelCase__			  =      loss_fct(lowerCamelCase_   ,       lowerCamelCase_  )

														if not return_dict:
																					lowerCAmelCase__			  =      (logits,) + outputs[2:]
																					return ((loss,) + output) if loss is not None else output

														return ImageClassifierOutputWithNoAttention(
														    loss=lowerCamelCase_   ,       logits=lowerCamelCase_   ,       hidden_states=outputs.hidden_states   ,       )

'''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


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

snake_case									=    """ Hello world! cécé herlolip"""

snake_case									=    [
    ("""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        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	[
     "encoder.version",
     "decoder.version",
     "model.encoder.version",
     "model.decoder.version",
     "_float_tensor",
 ]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
 lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			"bart.large.cnn"			).eval()
 hub_interface.model.load_state_dict(sd["model"]			)
 return hub_interface


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	emb.weight.shape
 lowerCAmelCase__     :   List[Any]     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if not os.path.exists(lowerCamelCase_			):
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			lowerCamelCase_			).eval()
 else:
  lowerCAmelCase__     :   Tuple     		=	load_xsum_checkpoint(lowerCamelCase_			)

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





