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()
snake_case__					:      Optional[int]				      =  logging.get_logger(__name__)

snake_case__					:      int				      =  '''Hello world! cécé herlolip'''


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				FairseqRobertaModel.from_pretrained(UpperCAmelCase__       )
     roberta.eval()  # disable dropout
     UpperCAmelCase_							:       List[str]       =				roberta.model.encoder.sentence_encoder
     UpperCAmelCase_							:       Tuple       =				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_							:       List[Any]       =				roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

     print('Our RoBERTa config:'     ,					UpperCAmelCase__       )

     UpperCAmelCase_							:       int       =				XLMRobertaXLForSequenceClassification(UpperCAmelCase__       ) if classification_head else XLMRobertaXLForMaskedLM(UpperCAmelCase__       )
     model.eval()

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

     UpperCAmelCase_							:       str       =				roberta_sent_encoder.layer_norm.weight
     UpperCAmelCase_							:       Optional[int]       =				roberta_sent_encoder.layer_norm.bias

     for i in range(config.num_hidden_layers       ):
          # Encoder: start of layer
          UpperCAmelCase_							:       List[Any]       =				model.roberta.encoder.layer[i]
          UpperCAmelCase_							:       Union[str, Any]       =				roberta_sent_encoder.layers[i]

          UpperCAmelCase_							:       Dict       =				layer.attention
          UpperCAmelCase_							:       List[Any]       =				roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_							:       Optional[int]       =				roberta_layer.self_attn_layer_norm.bias

          # self attention
          UpperCAmelCase_							:       List[Any]       =				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_							:       Union[str, Any]       =				roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase_							:       Union[str, Any]       =				roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase_							:       List[str]       =				roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase_							:       Any       =				roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase_							:       Union[str, Any]       =				roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase_							:       str       =				roberta_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_							:       Union[str, Any]       =				layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase_							:       Optional[int]       =				roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase_							:       str       =				roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          UpperCAmelCase_							:       str       =				roberta_layer.final_layer_norm.weight
          UpperCAmelCase_							:       Any       =				roberta_layer.final_layer_norm.bias

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

          # output
          UpperCAmelCase_							:       int       =				layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_							:       int       =				roberta_layer.fca.weight
          UpperCAmelCase_							:       Any       =				roberta_layer.fca.bias
          # end of layer

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

     # Let's check that we get the same results.
     UpperCAmelCase_							:       Union[str, Any]       =				roberta.encode(UpperCAmelCase__       ).unsqueeze(0       )  # batch of size 1

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

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


if __name__ == "__main__":
 snake_case__					:      Any				      =  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__					:      Dict				      =  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 hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






