






"""simple docstring"""







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
 raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Union[str, Any]            =      '''Hello world! cécé herlolip'''






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			FairseqRobertaModel.from_pretrained(A__		)
       roberta.eval()  # disable dropout
       UpperCAmelCase	    =			roberta.model.encoder.sentence_encoder
       UpperCAmelCase	    =			XLMRobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,      hidden_size=roberta.cfg.model.encoder_embed_dim	,      num_hidden_layers=roberta.cfg.model.encoder_layers	,      num_attention_heads=roberta.cfg.model.encoder_attention_heads	,      intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim	,      max_position_embeddings=514	,      type_vocab_size=1	,      layer_norm_eps=1e-5	,      )
       if classification_head:
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

       print("""Our RoBERTa config:"""	,      A__		)

       UpperCAmelCase	    =			XLMRobertaXLForSequenceClassification(A__		) if classification_head else XLMRobertaXLForMaskedLM(A__		)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       UpperCAmelCase	    =			roberta_sent_encoder.embed_tokens.weight
       UpperCAmelCase	    =			roberta_sent_encoder.embed_positions.weight
       UpperCAmelCase	    =			torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.

       UpperCAmelCase	    =			roberta_sent_encoder.layer_norm.weight
       UpperCAmelCase	    =			roberta_sent_encoder.layer_norm.bias

       for i in range(config.num_hidden_layers		):
              # Encoder: start of layer
              UpperCAmelCase	    =			model.roberta.encoder.layer[i]
              UpperCAmelCase	    =			roberta_sent_encoder.layers[i]

              UpperCAmelCase	    =			layer.attention
              UpperCAmelCase	    =			roberta_layer.self_attn_layer_norm.weight
              UpperCAmelCase	    =			roberta_layer.self_attn_layer_norm.bias

              # self attention
              UpperCAmelCase	    =			layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)		)
              )

              UpperCAmelCase	    =			roberta_layer.self_attn.q_proj.weight
              UpperCAmelCase	    =			roberta_layer.self_attn.q_proj.bias
              UpperCAmelCase	    =			roberta_layer.self_attn.k_proj.weight
              UpperCAmelCase	    =			roberta_layer.self_attn.k_proj.bias
              UpperCAmelCase	    =			roberta_layer.self_attn.v_proj.weight
              UpperCAmelCase	    =			roberta_layer.self_attn.v_proj.bias

              # self-attention output
              UpperCAmelCase	    =			layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              UpperCAmelCase	    =			roberta_layer.self_attn.out_proj.weight
              UpperCAmelCase	    =			roberta_layer.self_attn.out_proj.bias

              # this one is final layer norm
              UpperCAmelCase	    =			roberta_layer.final_layer_norm.weight
              UpperCAmelCase	    =			roberta_layer.final_layer_norm.bias

              # intermediate
              UpperCAmelCase	    =			layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              UpperCAmelCase	    =			roberta_layer.fca.weight
              UpperCAmelCase	    =			roberta_layer.fca.bias

              # output
              UpperCAmelCase	    =			layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              UpperCAmelCase	    =			roberta_layer.fca.weight
              UpperCAmelCase	    =			roberta_layer.fca.bias
              # end of layer

       if classification_head:
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""].dense.weight
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""].dense.bias
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""].out_proj.weight
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""].out_proj.bias
       else:
              # LM Head
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.dense.weight
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.dense.bias
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.layer_norm.weight
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.layer_norm.bias
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.weight
              UpperCAmelCase	    =			roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       UpperCAmelCase	    =			roberta.encode(A__		).unsqueeze(0		)  # batch of size 1

       UpperCAmelCase	    =			model(A__		)[0]
       if classification_head:
              UpperCAmelCase	    =			roberta.model.classification_heads["""mnli"""](roberta.extract_features(A__		)		)
       else:
              UpperCAmelCase	    =			roberta.model(A__		)[0]
       print(our_output.shape	,      their_output.shape		)
       UpperCAmelCase	    =			torch.max(torch.abs(our_output - their_output		)		).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
       UpperCAmelCase	    =			torch.allclose(A__	,      A__	,      atol=1e-3		)
       print("""Do both models output the same tensors?"""	,      """🔥""" if success else """💩"""		)
       if not success:
              raise Exception("""Something went wRoNg"""		)

       pathlib.Path(A__		).mkdir(parents=A__	,      exist_ok=A__		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(A__		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Optional[int]            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
 )
 lowerCAmelCase_	:    Tuple            =      parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )








"""simple docstring"""







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()
lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

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

lowerCAmelCase_	:    int            =      {
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			EfficientNetConfig()
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""hidden_dim"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""width_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""depth_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dropout_rate"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dw_padding"""]

       UpperCAmelCase	    =			"""huggingface/label-files"""
       UpperCAmelCase	    =			"""imagenet-1k-id2label.json"""
       UpperCAmelCase	    =			1000
       UpperCAmelCase	    =			json.load(open(hf_hub_download(lowerCAmelCase	,      lowerCAmelCase	,      repo_type="""dataset"""		)	,      """r"""		)		)
       UpperCAmelCase	    =			{int(lowerCAmelCase		): v for k, v in idalabel.items()}

       UpperCAmelCase	    =			idalabel
       UpperCAmelCase	    =			{v: k for k, v in idalabel.items()}
       return config






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase	    =			Image.open(requests.get(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)
       return im






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			EfficientNetImageProcessor(
           size={"""height""": size, """width""": size}	,      image_mean=[0.4_85, 0.4_56, 0.4_06]	,      image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]	,      do_center_crop=lowerCAmelCase	,      )
       return preprocessor






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
       UpperCAmelCase	    =			sorted(set(lowerCAmelCase		)		)
       UpperCAmelCase	    =			len(lowerCAmelCase		)
       UpperCAmelCase	    =			{b: str(lowerCAmelCase		) for b, i in zip(lowerCAmelCase	,      range(lowerCAmelCase		)		)}

       UpperCAmelCase	    =			[]
       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	    =			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	    =			{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     UpperCAmelCase	    =			"""efficientnet.""" + item[1]

       UpperCAmelCase	    =			"""classifier.weight"""
       UpperCAmelCase	    =			"""classifier.bias"""
       return key_mapping






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

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

              # Replace HF parameters with original TF model parameters
              assert hf_params[hf_key].shape == new_hf_value.shape
              hf_params[hf_key].copy_(lowerCAmelCase		)






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			model_classes[model_name](
           include_top=lowerCAmelCase	,      weights="""imagenet"""	,      input_tensor=lowerCAmelCase	,      input_shape=lowerCAmelCase	,      pooling=lowerCAmelCase	,      classes=1000	,      classifier_activation="""softmax"""	,      )

       UpperCAmelCase	    =			original_model.trainable_variables
       UpperCAmelCase	    =			original_model.non_trainable_variables
       UpperCAmelCase	    =			{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              UpperCAmelCase	    =			param.numpy()
       UpperCAmelCase	    =			list(tf_params.keys()		)

       # Load HuggingFace model
       UpperCAmelCase	    =			get_efficientnet_config(lowerCAmelCase		)
       UpperCAmelCase	    =			EfficientNetForImageClassification(lowerCAmelCase		).eval()
       UpperCAmelCase	    =			hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""		)
       UpperCAmelCase	    =			rename_keys(lowerCAmelCase		)
       replace_params(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

       # Initialize preprocessor and preprocess input image
       UpperCAmelCase	    =			convert_image_processor(lowerCAmelCase		)
       UpperCAmelCase	    =			preprocessor(images=prepare_img()	,      return_tensors="""pt"""		)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              UpperCAmelCase	    =			hf_model(**lowerCAmelCase		)
       UpperCAmelCase	    =			outputs.logits.detach().numpy()

       # Original model inference
       UpperCAmelCase	    =			False
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			prepare_img().resize((image_size, image_size)	,      resample=PIL.Image.NEAREST		)
       UpperCAmelCase	    =			image.img_to_array(lowerCAmelCase		)
       UpperCAmelCase	    =			np.expand_dims(lowerCAmelCase	,      axis=0		)
       UpperCAmelCase	    =			original_model.predict(lowerCAmelCase		)

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(lowerCAmelCase	,      lowerCAmelCase	,      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(lowerCAmelCase		):
                     os.mkdir(lowerCAmelCase		)
              # Save converted model and image processor
              hf_model.save_pretrained(lowerCAmelCase		)
              preprocessor.save_pretrained(lowerCAmelCase		)

       if push_to_hub:
              # Push model and image processor to hub
              print(F'''Pushing converted {model_name} to the hub...'''		)
              UpperCAmelCase	    =			F'''efficientnet-{model_name}'''
              preprocessor.push_to_hub(lowerCAmelCase		)
              hf_model.push_to_hub(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    int            =      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''')

 lowerCAmelCase_	:    Optional[Any]            =      parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
