

'''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 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()
snake_case_       =       logging.get_logger(__name__)

snake_case_       =       'Hello world! cécé herlolip'


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	bool    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Tuple     =       FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
      roberta.eval()  # disable dropout
      SCREAMING_SNAKE_CASE_      :			Tuple     =       roberta.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE_      :			Dict     =       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=5_1_4  ,  type_vocab_size=1  ,  layer_norm_eps=1E-5  ,  )
      if classification_head:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our BERT config:"  ,  SCREAMING_SNAKE_CASE_    )

      SCREAMING_SNAKE_CASE_      :			str     =       RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_    ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_    )
      model.eval()

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

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

            # self attention
            SCREAMING_SNAKE_CASE_      :			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)    )
            )

            SCREAMING_SNAKE_CASE_      :			str     =       roberta_layer.self_attn.q_proj.weight
            SCREAMING_SNAKE_CASE_      :			Tuple     =       roberta_layer.self_attn.q_proj.bias
            SCREAMING_SNAKE_CASE_      :			Dict     =       roberta_layer.self_attn.k_proj.weight
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       roberta_layer.self_attn.k_proj.bias
            SCREAMING_SNAKE_CASE_      :			Any     =       roberta_layer.self_attn.v_proj.weight
            SCREAMING_SNAKE_CASE_      :			Tuple     =       roberta_layer.self_attn.v_proj.bias

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

            # intermediate
            SCREAMING_SNAKE_CASE_      :			BertIntermediate     =       layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       roberta_layer.fca.weight
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       roberta_layer.fca.bias

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

      if classification_head:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       roberta.model.classification_heads["mnli"].dense.weight
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       roberta.model.classification_heads["mnli"].dense.bias
            SCREAMING_SNAKE_CASE_      :			Any     =       roberta.model.classification_heads["mnli"].out_proj.weight
            SCREAMING_SNAKE_CASE_      :			str     =       roberta.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       roberta.model.encoder.lm_head.dense.weight
            SCREAMING_SNAKE_CASE_      :			List[str]     =       roberta.model.encoder.lm_head.dense.bias
            SCREAMING_SNAKE_CASE_      :			int     =       roberta.model.encoder.lm_head.layer_norm.weight
            SCREAMING_SNAKE_CASE_      :			str     =       roberta.model.encoder.lm_head.layer_norm.bias
            SCREAMING_SNAKE_CASE_      :			List[str]     =       roberta.model.encoder.lm_head.weight
            SCREAMING_SNAKE_CASE_      :			int     =       roberta.model.encoder.lm_head.bias

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

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       model(SCREAMING_SNAKE_CASE_    )[0]
      if classification_head:
            SCREAMING_SNAKE_CASE_      :			str     =       roberta.model.classification_heads["mnli"](roberta.extract_features(SCREAMING_SNAKE_CASE_    )    )
      else:
            SCREAMING_SNAKE_CASE_      :			str     =       roberta.model(SCREAMING_SNAKE_CASE_    )[0]
      print(our_output.shape  ,  their_output.shape    )
      SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.max(torch.abs(our_output - their_output    )    ).item()
      print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
      SCREAMING_SNAKE_CASE_      :			List[str]     =       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__":
      snake_case_       =       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.'
      )
      snake_case_       =       parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )




'''simple docstring'''






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 MobileNetVaImageProcessor
class 	SCREAMING_SNAKE_CASE__			(				unittest.TestCase      ):




    def __init__(					self				,     lowercase__				,     lowercase__=7				,     lowercase__=3				,     lowercase__=18				,     lowercase__=30				,     lowercase__=400				,     lowercase__=True				,     lowercase__=None				,     lowercase__=True				,     lowercase__=None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       size if size is not None else {"shortest_edge": 20}
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       crop_size if crop_size is not None else {"height": 18, "width": 18}
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       parent
          SCREAMING_SNAKE_CASE_      :			Tuple     =       batch_size
          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Dict     =       min_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max_resolution
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       do_resize
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       size
          SCREAMING_SNAKE_CASE_      :			Tuple     =       do_center_crop
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       crop_size







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,unittest.TestCase      ):
    _A			    =							MobileNetVaImageProcessor if is_vision_available() else None




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       MobileNetVaImageProcessingTester(self			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.image_processor_tester.prepare_image_processor_dict()




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowercase__				,     "do_resize"			)			)
          self.assertTrue(hasattr(lowercase__				,     "size"			)			)
          self.assertTrue(hasattr(lowercase__				,     "do_center_crop"			)			)
          self.assertTrue(hasattr(lowercase__				,     "center_crop"			)			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 20}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 18, "width": 18}			)

          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84			)
          self.assertEqual(image_processor.size				,     {"shortest_edge": 42}			)
          self.assertEqual(image_processor.crop_size				,     {"height": 84, "width": 84}			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          pass




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          SCREAMING_SNAKE_CASE_      :			Union[str, 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
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			List[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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_      :			Dict     =       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
          SCREAMING_SNAKE_CASE_      :			Dict     =       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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			Dict     =       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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_      :			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
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

          # Test batched
          SCREAMING_SNAKE_CASE_      :			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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )				,     )

