



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


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

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''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	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

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

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


if __name__ == "__main__":
    __UpperCAmelCase          =       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'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__UpperCAmelCase          =       {
    '''b0''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1_408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1_536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1_792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2_304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2_560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}
def 		UpperCamelCase	(   snake_case__       :    int	)	->     Optional[int]:
   UpperCamelCase    :  str							=	EfficientNetConfig()
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['hidden_dim']
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['width_coef']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['depth_coef']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['dropout_rate']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['dw_padding']

   UpperCamelCase    :  str							=	'huggingface/label-files'
   UpperCamelCase    :  Optional[Any]							=	'imagenet-1k-id2label.json'
   UpperCamelCase    :  Optional[Any]							=	1000
   UpperCamelCase    :  Dict							=	json.load(open(hf_hub_download(snake_case__       ,     snake_case__       ,     repo_type='dataset'	)       ,     'r'	)	)
   UpperCamelCase    :  Tuple							=	{int(snake_case__	): v for k, v in idalabel.items()}

   UpperCamelCase    :  Optional[int]							=	idalabel
   UpperCamelCase    :  Tuple							=	{v: k for k, v in idalabel.items()}
   return config
def 		UpperCamelCase	(   )	->     Tuple:
   UpperCamelCase    :  Optional[Any]							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  Union[str, Any]							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   return im
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     List[Any]:
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	EfficientNetImageProcessor(
       size={'height': size, 'width': size}       ,     image_mean=[0.485, 0.456, 0.406]       ,     image_std=[0.47853944, 0.4732864, 0.47434163]       ,     do_center_crop=snake_case__       ,     )
   return preprocessor
def 		UpperCamelCase	(   snake_case__       :    Optional[int]	)	->     Dict:
   UpperCamelCase    :  int							=	[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
   UpperCamelCase    :  str							=	sorted(set(snake_case__	)	)
   UpperCamelCase    :  int							=	len(snake_case__	)
   UpperCamelCase    :  str							=	{b: str(snake_case__	) for b, i in zip(snake_case__       ,     range(snake_case__	)	)}

   UpperCamelCase    :  Optional[int]							=	[]
   rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
   rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
   rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
   rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
   rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

   for b in block_names:
      UpperCamelCase    :  Union[str, Any]							=	block_name_mapping[b]
      rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")	)
      rename_keys.append(
          (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")	)
      rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")	)
      rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")	)

      rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")	)
      rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")	)
      rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")	)
      rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")	)

   rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
   rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
   rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
   rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
   rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

   UpperCamelCase    :  List[str]							=	{}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCamelCase    :  Dict							=	'efficientnet.' + item[1]

   UpperCamelCase    :  Dict							=	'classifier.weight'
   UpperCamelCase    :  Dict							=	'classifier.bias'
   return key_mapping
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    int	)	->     Dict:
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      UpperCamelCase    :  Any							=	key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	).permute(3       ,     2       ,     0       ,     1	)
      elif "depthwise_kernel" in key:
         UpperCamelCase    :  Any							=	torch.from_numpy(snake_case__	).permute(2       ,     3       ,     0       ,     1	)
      elif "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(np.transpose(snake_case__	)	)
      else:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	)

      # Replace HF parameters with original TF model parameters
      assert hf_params[hf_key].shape == new_hf_value.shape
      hf_params[hf_key].copy_(snake_case__	)
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    int       ,     snake_case__       :    Optional[int]	)	->     Any:
   UpperCamelCase    :  Union[str, Any]							=	model_classes[model_name](
       include_top=snake_case__       ,     weights='imagenet'       ,     input_tensor=snake_case__       ,     input_shape=snake_case__       ,     pooling=snake_case__       ,     classes=1000       ,     classifier_activation='softmax'       ,     )

   UpperCamelCase    :  Optional[int]							=	original_model.trainable_variables
   UpperCamelCase    :  Optional[int]							=	original_model.non_trainable_variables
   UpperCamelCase    :  Tuple							=	{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      UpperCamelCase    :  List[Any]							=	param.numpy()
   UpperCamelCase    :  List[str]							=	list(tf_params.keys()	)

   # Load HuggingFace model
   UpperCamelCase    :  str							=	get_efficientnet_config(snake_case__	)
   UpperCamelCase    :  Any							=	EfficientNetForImageClassification(snake_case__	).eval()
   UpperCamelCase    :  Tuple							=	hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('Converting parameters...'	)
   UpperCamelCase    :  List[Any]							=	rename_keys(snake_case__	)
   replace_params(snake_case__       ,     snake_case__       ,     snake_case__	)

   # Initialize preprocessor and preprocess input image
   UpperCamelCase    :  List[Any]							=	convert_image_processor(snake_case__	)
   UpperCamelCase    :  Dict							=	preprocessor(images=prepare_img()       ,     return_tensors='pt'	)

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCamelCase    :  Optional[int]							=	hf_model(**snake_case__	)
   UpperCamelCase    :  Dict							=	outputs.logits.detach().numpy()

   # Original model inference
   UpperCamelCase    :  Optional[int]							=	False
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[Any]							=	prepare_img().resize((image_size, image_size)       ,     resample=PIL.Image.NEAREST	)
   UpperCamelCase    :  List[Any]							=	image.img_to_array(snake_case__	)
   UpperCamelCase    :  str							=	np.expand_dims(snake_case__       ,     axis=0	)
   UpperCamelCase    :  Any							=	original_model.predict(snake_case__	)

   # Check whether original and HF model outputs match  -> np.allclose
   assert np.allclose(snake_case__       ,     snake_case__       ,     atol=1E-3	), "The predicted logits are not the same."
   print('Model outputs match!'	)

   if save_model:
      # Create folder to save model
      if not os.path.isdir(snake_case__	):
         os.mkdir(snake_case__	)
      # Save converted model and image processor
      hf_model.save_pretrained(snake_case__	)
      preprocessor.save_pretrained(snake_case__	)

   if push_to_hub:
      # Push model and image processor to hub
      print(F"""Pushing converted {model_name} to the hub..."""	)
      UpperCamelCase    :  List[str]							=	F"""efficientnet-{model_name}"""
      preprocessor.push_to_hub(snake_case__	)
      hf_model.push_to_hub(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''b0''',
        type=str,
        help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''hf_model''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

    __UpperCAmelCase          =       parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



