


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
 )






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ConvNextImageProcessor




class A						(     unittest.TestCase  ):






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  str    ,     lowercase_		:  Tuple=7    ,     lowercase_		:  int=3    ,     lowercase_		:  Union[str, Any]=18    ,     lowercase_		:  Optional[int]=30    ,     lowercase_		:  Tuple=400    ,     lowercase_		:  str=True    ,     lowercase_		:  Dict=None    ,     lowercase_		:  Tuple=0.875    ,     lowercase_		:  List[Any]=True    ,     lowercase_		:  Union[str, Any]=[0.5, 0.5, 0.5]    ,     lowercase_		:  List[str]=[0.5, 0.5, 0.5]    ,     )      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=size if size is not None else {'shortest_edge': 20}
             _lowerCamelCase :	Optional[Any]						=parent
             _lowerCamelCase :	Union[str, Any]						=batch_size
             _lowerCamelCase :	Dict						=num_channels
             _lowerCamelCase :	Union[str, Any]						=image_size
             _lowerCamelCase :	Optional[int]						=min_resolution
             _lowerCamelCase :	Tuple						=max_resolution
             _lowerCamelCase :	Any						=do_resize
             _lowerCamelCase :	Dict						=size
             _lowerCamelCase :	int						=crop_pct
             _lowerCamelCase :	Dict						=do_normalize
             _lowerCamelCase :	int						=image_mean
             _lowerCamelCase :	Union[str, Any]						=image_std






      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_pct": self.crop_pct,
             }




@require_torch
@require_vision
class A						(     UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						List[Any]   =ConvNextImageProcessor if is_vision_available() else None






      def  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=ConvNextImageProcessingTester(self				)






      @property
      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.image_processing_class(**self.image_processor_dict				)
             self.assertTrue(hasattr(lowercase_    ,     'do_resize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'size'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'crop_pct'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'do_normalize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_mean'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_std'				)				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.image_processing_class.from_dict(self.image_processor_dict				)
             self.assertEqual(image_processor.size    ,     {'shortest_edge': 20}				)

             _lowerCamelCase :	Tuple						=self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42				)
             self.assertEqual(image_processor.size    ,     {'shortest_edge': 42}				)






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             pass






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PIL images
             _lowerCamelCase :	Any						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     Image.Image				)

             # Test not batched input
             _lowerCamelCase :	Tuple						=image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	List[str]						=image_processing(lowercase_    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )






      def  lowerCamelCase (      self		:  int				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.image_processing_class(**self.image_processor_dict				)
             # create random numpy tensors
             _lowerCamelCase :	List[Any]						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_    ,     numpify=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     np.ndarray				)

             # Test not batched input
             _lowerCamelCase :	Optional[Any]						=image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	Any						=image_processing(lowercase_    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )






      def  lowerCamelCase (      self		:  str				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             _lowerCamelCase :	Any						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_    ,     torchify=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     torch.Tensor				)

             # Test not batched input
             _lowerCamelCase :	Optional[Any]						=image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )

             # Test batched
             _lowerCamelCase :	Union[str, Any]						=image_processing(lowercase_    ,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape    ,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['shortest_edge'],
                     self.image_processor_tester.size['shortest_edge'],
                 )    ,     )


