


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=30				,       _SCREAMING_SNAKE_CASE=400				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=1 / 255				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]				,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]				,       _SCREAMING_SNAKE_CASE=True				,       )->List[Any]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
         A_      :				Any		     =					parent
         A_      :				Dict		     =					batch_size
         A_      :				List[str]		     =					num_channels
         A_      :				Optional[int]		     =					min_resolution
         A_      :				Any		     =					max_resolution
         A_      :				Tuple		     =					do_resize
         A_      :				Optional[int]		     =					size
         A_      :				List[Any]		     =					do_rescale
         A_      :				List[str]		     =					rescale_factor
         A_      :				Any		     =					do_normalize
         A_      :				Union[str, Any]		     =					image_mean
         A_      :				List[Any]		     =					image_std
         A_      :				List[Any]		     =					do_pad



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Optional[int]:




         '''simple docstring'''





         if not batched:
           A_      :				List[Any]		     =					image_inputs[0]
           if isinstance(_SCREAMING_SNAKE_CASE				,       Image.Image		):
             A_     ,       A_      :				List[str]		     =					image.size
           else:
             A_     ,       A_      :				str		     =					image.shape[1], image.shape[2]
           if w < h:
             A_      :				Tuple		     =					int(self.size['''shortest_edge'''] * h / w		)
             A_      :				List[Any]		     =					self.size['''shortest_edge''']
           elif w > h:
             A_      :				Tuple		     =					self.size['''shortest_edge''']
             A_      :				Union[str, Any]		     =					int(self.size['''shortest_edge'''] * w / h		)
           else:
             A_      :				List[Any]		     =					self.size['''shortest_edge''']
             A_      :				str		     =					self.size['''shortest_edge''']

         else:
           A_      :				Tuple		     =					[]
           for image in image_inputs:
             A_     ,       A_      :				Tuple		     =					self.get_expected_values([image]		)
             expected_values.append((expected_height, expected_width)		)
           A_      :				str		     =					max(_SCREAMING_SNAKE_CASE				,       key=lambda _SCREAMING_SNAKE_CASE		: item[0]		)[0]
           A_      :				Optional[Any]		     =					max(_SCREAMING_SNAKE_CASE				,       key=lambda _SCREAMING_SNAKE_CASE		: item[1]		)[1]

         return expected_height, expected_width






@require_torch
@require_vision
class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DetrImageProcessor if is_vision_available() else None



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					DetrImageProcessingTester(self		)



       @property
       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				List[Any]		     =					self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''image_mean'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''image_std'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''do_normalize'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''do_rescale'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''rescale_factor'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''do_resize'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''size'''		)		)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''do_pad'''		)		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				int		     =					self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size				,       {'''shortest_edge''': 18, '''longest_edge''': 1333}		)
         self.assertEqual(image_processor.do_pad				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					self.image_processing_class.from_dict(
             self.image_processor_dict				,       size=42				,       max_size=84				,       pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE		)
         self.assertEqual(image_processor.size				,       {'''shortest_edge''': 42, '''longest_edge''': 84}		)
         self.assertEqual(image_processor.do_pad				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         pass



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





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

         # Test not batched input
         A_      :				Optional[int]		     =					image_processing(image_inputs[0]				,       return_tensors='''pt'''		).pixel_values

         A_     ,       A_      :				Any		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE		)

         self.assertEqual(
             encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

         # Test batched
         A_     ,       A_      :				Any		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				,       batched=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					image_processing(_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_images.shape				,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,       )



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				int		     =					self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         A_      :				Union[str, Any]		     =					prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_SCREAMING_SNAKE_CASE				,       numpify=_SCREAMING_SNAKE_CASE		)
         for image in image_inputs:
           self.assertIsInstance(_SCREAMING_SNAKE_CASE				,       np.ndarray		)

         # Test not batched input
         A_      :				Tuple		     =					image_processing(image_inputs[0]				,       return_tensors='''pt'''		).pixel_values

         A_     ,       A_      :				List[Any]		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE		)

         self.assertEqual(
             encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

         # Test batched
         A_      :				List[str]		     =					image_processing(_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).pixel_values

         A_     ,       A_      :				Optional[Any]		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				,       batched=_SCREAMING_SNAKE_CASE		)

         self.assertEqual(
             encoded_images.shape				,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,       )



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





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

         # Test not batched input
         A_      :				Any		     =					image_processing(image_inputs[0]				,       return_tensors='''pt'''		).pixel_values

         A_     ,       A_      :				Optional[Any]		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE		)

         self.assertEqual(
             encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

         # Test batched
         A_      :				Union[str, Any]		     =					image_processing(_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).pixel_values

         A_     ,       A_      :				Dict		     =					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				,       batched=_SCREAMING_SNAKE_CASE		)

         self.assertEqual(
             encoded_images.shape				,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )				,       )



       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Optional[int]		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,       '''r'''		) as f:
           A_      :				List[Any]		     =					json.loads(f.read()		)

         A_      :				Any		     =					{'''image_id''': 3_9769, '''annotations''': target}

         # encode them
         A_      :				Optional[int]		     =					DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''		)
         A_      :				Optional[int]		     =					image_processing(images=_SCREAMING_SNAKE_CASE				,       annotations=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		)

         # verify pixel values
         A_      :				List[str]		     =					torch.Size([1, 3, 800, 1066]		)
         self.assertEqual(encoding['''pixel_values'''].shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-4		)		)

         # verify area
         A_      :				Any		     =					torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify boxes
         A_      :				Optional[int]		     =					torch.Size([6, 4]		)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-3		)		)
         # verify image_id
         A_      :				Optional[int]		     =					torch.tensor([3_9769]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify is_crowd
         A_      :				Union[str, Any]		     =					torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify class_labels
         A_      :				int		     =					torch.tensor([75, 75, 63, 65, 17, 17]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify orig_size
         A_      :				Union[str, Any]		     =					torch.tensor([480, 640]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify size
         A_      :				List[Any]		     =					torch.tensor([800, 1066]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,       _SCREAMING_SNAKE_CASE		)		)







       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,       '''r'''		) as f:
           A_      :				Union[str, Any]		     =					json.loads(f.read()		)

         A_      :				Optional[int]		     =					{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

         A_      :				List[str]		     =					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

         # encode them
         A_      :				Union[str, Any]		     =					DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''		)
         A_      :				List[Any]		     =					image_processing(images=_SCREAMING_SNAKE_CASE				,       annotations=_SCREAMING_SNAKE_CASE				,       masks_path=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		)

         # verify pixel values
         A_      :				List[Any]		     =					torch.Size([1, 3, 800, 1066]		)
         self.assertEqual(encoding['''pixel_values'''].shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				int		     =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-4		)		)

         # verify area
         A_      :				int		     =					torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify boxes
         A_      :				int		     =					torch.Size([6, 4]		)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-3		)		)
         # verify image_id
         A_      :				Optional[Any]		     =					torch.tensor([3_9769]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify is_crowd
         A_      :				str		     =					torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify class_labels
         A_      :				Optional[Any]		     =					torch.tensor([17, 17, 63, 75, 75, 93]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify masks
         A_      :				Optional[Any]		     =					82_2873
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,       _SCREAMING_SNAKE_CASE		)
         # verify orig_size
         A_      :				Tuple		     =					torch.tensor([480, 640]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,       _SCREAMING_SNAKE_CASE		)		)
         # verify size
         A_      :				List[Any]		     =					torch.tensor([800, 1066]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,       _SCREAMING_SNAKE_CASE		)		)








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()
UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	"""Hello world! cécé herlolip"""


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE       )
  roberta.eval()  # disable dropout
  A_      :				Dict		     =					roberta.model.encoder.sentence_encoder
  A_      :				Optional[Any]		     =					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:
    A_      :				Optional[int]		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:''' ,			SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE       ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE       )
  model.eval()

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

  A_      :				Union[str, Any]		     =					roberta_sent_encoder.layer_norm.weight
  A_      :				int		     =					roberta_sent_encoder.layer_norm.bias

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

    A_      :				RobertaAttention		     =					layer.attention
    A_      :				Dict		     =					roberta_layer.self_attn_layer_norm.weight
    A_      :				str		     =					roberta_layer.self_attn_layer_norm.bias

    # self attention
    A_      :				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)       )
    )

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

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

    # this one is final layer norm
    A_      :				Any		     =					roberta_layer.final_layer_norm.weight
    A_      :				int		     =					roberta_layer.final_layer_norm.bias

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

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

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

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

  A_      :				Union[str, Any]		     =					model(SCREAMING_SNAKE_CASE       )[0]
  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE       )       )
  else:
    A_      :				int		     =					roberta.model(SCREAMING_SNAKE_CASE       )[0]
  print(our_output.shape ,			their_output.shape       )
  A_      :				Any		     =					torch.max(torch.abs(our_output - their_output       )       ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
  A_      :				Tuple		     =					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__":
     UpperCamelCase								=	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."""
     )
     UpperCamelCase								=	parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )




