


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


lowerCamelCase		      = {
    'vit_b32': {
        'embed_dim': 5_12,
        'image_resolution': 7_68,
        'context_length': 16,
        'vocab_size': 4_94_08,
        'vision_layers': 12,
        'vision_width': 7_68,
        'vision_patch_size': 32,
        'transformer_width': 5_12,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 5_12,
        'image_resolution': 7_68,
        'context_length': 16,
        'vocab_size': 4_94_08,
        'vision_layers': 12,
        'vision_width': 7_68,
        'vision_patch_size': 16,
        'transformer_width': 5_12,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 7_68,
        'image_resolution': 8_40,
        'context_length': 16,
        'vocab_size': 4_94_08,
        'vision_layers': 24,
        'vision_width': 10_24,
        'vision_patch_size': 14,
        'transformer_width': 7_68,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[Any]	,    SCREAMING_SNAKE_CASE__      :   str=""	,    SCREAMING_SNAKE_CASE__      :   Any="/"  ):
       '''simple docstring'''





       _lowerCamelCase :	Any						=[]

       for k, v in params.items():
              _lowerCamelCase :	int						=parent_key + sep + k if parent_key else k

              if isinstance(SCREAMING_SNAKE_CASE__	,    collections.MutableMapping  ):
                     items.extend(flatten_nested_dict(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    sep=SCREAMING_SNAKE_CASE__  ).items()  )
              else:
                     items.append((new_key, v)  )
       return dict(SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]  ):
       '''simple docstring'''





       return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__  : x.astype(jnp.floataa  ) if x.dtype == jnp.bfloataa else x	,    SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
       '''simple docstring'''





       _lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	int						=pt_attn_layer.in_proj_weight.chunk(3	,    dim=0  )
       _lowerCamelCase			,  _lowerCamelCase			,  _lowerCamelCase :	List[str]						=pt_attn_layer.in_proj_bias.chunk(3	,    dim=0  )

       _lowerCamelCase :	List[str]						=pt_attn_layer.out_proj.weight
       _lowerCamelCase :	Union[str, Any]						=pt_attn_layer.out_proj.bias

       _lowerCamelCase :	Union[str, Any]						=q_proj
       _lowerCamelCase :	Dict						=q_proj_bias

       _lowerCamelCase :	Union[str, Any]						=k_proj
       _lowerCamelCase :	Any						=k_proj_bias

       _lowerCamelCase :	int						=v_proj
       _lowerCamelCase :	Optional[int]						=v_proj_bias

       _lowerCamelCase :	Tuple						=out_proj_weights
       _lowerCamelCase :	Union[str, Any]						=out_proj_bias




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   List[Any]  ):
       '''simple docstring'''





       copy_linear(hf_mlp.fca	,    pt_mlp.c_fc  )
       copy_linear(hf_mlp.fca	,    pt_mlp.c_proj  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[int]	,    SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=pt_linear.weight
       _lowerCamelCase :	Optional[Any]						=pt_linear.bias




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       copy_linear(hf_layer.layer_norma	,    pt_layer.ln_a  )
       copy_linear(hf_layer.layer_norma	,    pt_layer.ln_a  )

       # copy MLP
       copy_mlp(hf_layer.mlp	,    pt_layer.mlp  )

       # copy attn
       copy_attn_layer(hf_layer.self_attn	,    pt_layer.attn  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              copy_layer(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   Dict  ):
       '''simple docstring'''





       _lowerCamelCase :	str						=pt_model.token_embedding.weight
       _lowerCamelCase :	Dict						=pt_model.positional_embedding

       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm	,    pt_model.ln_final  )

       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers	,    pt_model.transformer.resblocks  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Dict	,    SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=pt_model.text_projection.data.T

       # copy text encoder
       copy_encoder(hf_model.text_model	,    SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Dict	,    SCREAMING_SNAKE_CASE__      :   List[str]  ):
       '''simple docstring'''





       _lowerCamelCase :	int						=pt_model.visual.proj.data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm	,    pt_model.visual.ln_pre  )
       copy_linear(hf_model.vision_model.post_layernorm	,    pt_model.visual.ln_post  )

       # copy embeds
       _lowerCamelCase :	List[Any]						=pt_model.visual.conva.weight.data
       _lowerCamelCase :	Optional[int]						=pt_model.visual.class_embedding
       _lowerCamelCase :	Union[str, Any]						=pt_model.visual.positional_embedding.data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers	,    pt_model.visual.transformer.resblocks  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   List[str]  ):
       '''simple docstring'''





       _lowerCamelCase :	Union[str, Any]						=flatten_nested_dict(flax_params['backbone']['merged_class_token']  )

       _lowerCamelCase :	str						=torch.from_numpy(flax_class_token_params['scale']  )
       _lowerCamelCase :	List[str]						=torch.from_numpy(flax_class_token_params['bias']  )
       _lowerCamelCase :	str						=nn.Parameter(SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	List[str]						=nn.Parameter(SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   Any  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=hf_model.state_dict()
       _lowerCamelCase :	List[Any]						={}

       # Rename class prediction head flax params to pytorch HF
       _lowerCamelCase :	List[Any]						=flatten_nested_dict(flax_params['class_head']  )

       for flax_key, v in flax_class_params.items():
              _lowerCamelCase :	Optional[Any]						=flax_key.replace('/'	,    '.'  )
              _lowerCamelCase :	int						=torch_key.replace('.kernel'	,    '.weight'  )
              _lowerCamelCase :	Union[str, Any]						=torch_key.replace('Dense_0'	,    'dense0'  )
              _lowerCamelCase :	Tuple						='class_head.' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     _lowerCamelCase :	List[str]						=v.T

              _lowerCamelCase :	List[Any]						=nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__  )  )

       # Rename box prediction box flax params to pytorch HF
       _lowerCamelCase :	List[str]						=flatten_nested_dict(flax_params['obj_box_head']  )

       for flax_key, v in flax_box_params.items():
              _lowerCamelCase :	Union[str, Any]						=flax_key.replace('/'	,    '.'  )
              _lowerCamelCase :	Any						=torch_key.replace('.kernel'	,    '.weight'  )
              _lowerCamelCase :	int						=torch_key.replace('_'	,    ''  ).lower()
              _lowerCamelCase :	Optional[int]						='box_head.' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     _lowerCamelCase :	Optional[Any]						=v.T

              _lowerCamelCase :	Tuple						=nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__  )  )

       # Copy flax params to PyTorch params
       for name, param in new_params.items():
              if name in pt_params.keys():
                     pt_params[name].copy_(SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





       for k, v in flax_attn_params.items():
              if k.startswith('transformer'  ):
                     _lowerCamelCase :	List[str]						=k.replace('transformer.resblocks'	,    'text_model.encoder.layers'  )
              else:
                     _lowerCamelCase :	Union[str, Any]						=k.replace('visual.transformer.resblocks'	,    'vision_model.encoder.layers'  )

              _lowerCamelCase :	Optional[Any]						=torch_key.replace('attn'	,    'self_attn'  )
              _lowerCamelCase :	Tuple						=torch_key.replace('key'	,    'k_proj'  )
              _lowerCamelCase :	str						=torch_key.replace('value'	,    'v_proj'  )
              _lowerCamelCase :	Optional[int]						=torch_key.replace('query'	,    'q_proj'  )
              _lowerCamelCase :	Union[str, Any]						=torch_key.replace('out'	,    'out_proj'  )

              if "bias" in torch_key and v.ndim == 2:
                     _lowerCamelCase :	str						=v.shape[0] * v.shape[1]
                     _lowerCamelCase :	Any						=v.reshape(SCREAMING_SNAKE_CASE__  )

              if "weight" in torch_key and "out" in torch_key:
                     _lowerCamelCase :	Optional[Any]						=(v.shape[0] * v.shape[1], v.shape[2])
                     _lowerCamelCase :	Optional[Any]						=v.reshape(SCREAMING_SNAKE_CASE__  ).T

              if "weight" in torch_key and "out" not in torch_key:
                     _lowerCamelCase :	Union[str, Any]						=(v.shape[0], v.shape[1] * v.shape[2])
                     _lowerCamelCase :	Union[str, Any]						=v.reshape(SCREAMING_SNAKE_CASE__  ).T

              # Copy flax CLIP attn params to HF PyTorch params
              _lowerCamelCase :	int						=torch.from_numpy(SCREAMING_SNAKE_CASE__  )
              hf_backbone.state_dict()[torch_key].copy_(SCREAMING_SNAKE_CASE__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[str]  ):
       '''simple docstring'''





       _lowerCamelCase :	List[Any]						={}
       _lowerCamelCase :	Tuple						=[]

       for k, v in params.items():
              if "attn." in k:
                     _lowerCamelCase :	Tuple						=k[: k.rindex('attn.'  ) + 5]
                     if base in processed_attn_layers:
                            continue

                     processed_attn_layers.append(SCREAMING_SNAKE_CASE__  )
                     _lowerCamelCase :	Dict						=params[base + 'out.weight'].shape[-1]
                     _lowerCamelCase :	Optional[Any]						=params[base + 'out.weight'].reshape(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ).T
                     _lowerCamelCase :	Union[str, Any]						=params[base + 'out.bias']
              else:
                     _lowerCamelCase :	Optional[Any]						=v
       return new_params




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       _lowerCamelCase :	str						=CLIP(**SCREAMING_SNAKE_CASE__  )
       torch_model.eval()
       _lowerCamelCase :	Optional[int]						=torch_model.state_dict()

       _lowerCamelCase :	List[str]						=flatten_nested_dict(flax_params['backbone']['clip']  )
       _lowerCamelCase :	List[Any]						={}

       for flax_key, v in flax_clip_params.items():
              _lowerCamelCase :	str						=flax_key.replace('/'	,    '.'  )
              _lowerCamelCase :	int						=torch_key.replace('text.token_embedding.embedding'	,    'token_embedding.kernel'  )

              if (
                  torch_key.startswith('text.transformer'  )
                  or torch_key.startswith('text.text_projection'  )
                  or torch_key.startswith('text.ln_final'  )
                  or torch_key.startswith('text.positional_embedding'  )
              ):
                     _lowerCamelCase :	Tuple						=torch_key[5:]

              _lowerCamelCase :	List[str]						=torch_key.replace('text_projection.kernel'	,    'text_projection'  )
              _lowerCamelCase :	Any						=torch_key.replace('visual.proj.kernel'	,    'visual.proj'  )
              _lowerCamelCase :	List[Any]						=torch_key.replace('.scale'	,    '.weight'  )
              _lowerCamelCase :	Union[str, Any]						=torch_key.replace('.kernel'	,    '.weight'  )

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     _lowerCamelCase :	Optional[Any]						=v.transpose(3	,    2	,    0	,    1  )

              elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                     # Fully connected layers are transposed, embeddings are not
                     _lowerCamelCase :	Optional[Any]						=v.T

              _lowerCamelCase :	Dict						=v

       _lowerCamelCase :	int						=_convert_attn_layers(SCREAMING_SNAKE_CASE__  )
       new_torch_params.update(SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Optional[int]						={}

       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
              if name in torch_clip_params.keys():
                     _lowerCamelCase :	Optional[int]						=torch.from_numpy(new_torch_params[name]  )
                     torch_clip_params[name].copy_(SCREAMING_SNAKE_CASE__  )
              else:
                     _lowerCamelCase :	Any						=param

       return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   Optional[Any]	,    SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   Optional[int]=None  ):
       '''simple docstring'''





       _lowerCamelCase :	List[Any]						=Repository(SCREAMING_SNAKE_CASE__	,    clone_from=F'''google/{pytorch_dump_folder_path}'''  )
       repo.git_pull()

       if config_path is not None:
              _lowerCamelCase :	Union[str, Any]						=OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE__  )
       else:
              _lowerCamelCase :	Optional[Any]						=OwlViTConfig()

       _lowerCamelCase :	Dict						=OwlViTModel(SCREAMING_SNAKE_CASE__  ).eval()
       _lowerCamelCase :	List[Any]						=OwlViTForObjectDetection(SCREAMING_SNAKE_CASE__  ).eval()

       copy_text_model_and_projection(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
       copy_vision_model_and_projection(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Optional[Any]						=pt_backbone.logit_scale
       copy_flax_attn_params(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=hf_backbone
       copy_class_merge_token(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
       copy_class_box_heads(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )

       # Save HF model
       hf_model.save_pretrained(repo.local_dir  )

       # Initialize image processor
       _lowerCamelCase :	str						=OwlViTImageProcessor(
           size=config.vision_config.image_size	,    crop_size=config.vision_config.image_size  )
       # Initialize tokenizer
       _lowerCamelCase :	Optional[Any]						=CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'	,    pad_token='!'	,    model_max_length=16  )

       # Initialize processor
       _lowerCamelCase :	Optional[int]						=OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE__	,    tokenizer=SCREAMING_SNAKE_CASE__  )
       image_processor.save_pretrained(repo.local_dir  )
       processor.save_pretrained(repo.local_dir  )

       repo.git_add()
       repo.git_commit('Upload model and processor'  )
       repo.git_push()


if __name__ == "__main__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--owlvit_version',
     default=None,
     type=str,
     required=True,
     help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
 )
 parser.add_argument(
     '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
 )
 parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
 parser.add_argument(
     '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
 )
 lowerCamelCase		      = parser.parse_args()

 # Initialize PyToch clip model
 lowerCamelCase		      = args.owlvit_version
 if model_name == "clip_b16":
  lowerCamelCase		      = CONFIGS['vit_b16']
 elif model_name == "clip_b32":
  lowerCamelCase		      = CONFIGS['vit_b32']
 elif model_name == "clip_l14":
  lowerCamelCase		      = CONFIGS['vit_l14']

 # Load from checkpoint and convert params to float-32
 lowerCamelCase		      = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
 lowerCamelCase		      = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
 del variables

 # Convert CLIP backbone
 lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase		      = convert_clip_backbone(flax_params, torch_config)

 convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)






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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


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

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase :	List[str]						=roberta_sent_encoder.embed_tokens.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.embed_positions.weight
       _lowerCamelCase :	Tuple						=torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
       _lowerCamelCase :	Optional[Any]						=roberta_sent_encoder.emb_layer_norm.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.emb_layer_norm.bias

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

              # self attention
              _lowerCamelCase :	BertSelfAttention						=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)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              _lowerCamelCase :	Any						=roberta.model.classification_heads['mnli'].dense.weight
              _lowerCamelCase :	Optional[Any]						=roberta.model.classification_heads['mnli'].dense.bias
              _lowerCamelCase :	List[str]						=roberta.model.classification_heads['mnli'].out_proj.weight
              _lowerCamelCase :	Tuple						=roberta.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _lowerCamelCase :	Optional[Any]						=roberta.model.encoder.lm_head.dense.weight
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.dense.bias
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.layer_norm.weight
              _lowerCamelCase :	int						=roberta.model.encoder.lm_head.layer_norm.bias
              _lowerCamelCase :	Dict						=roberta.model.encoder.lm_head.weight
              _lowerCamelCase :	str						=roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

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

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


if __name__ == "__main__":
 lowerCamelCase		      = 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		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


