"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 				a (						a__      ):


    def __init__(    self		:     List[Any]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Optional[int]=None      , __lowerCAmelCase		:     int=True      , __lowerCAmelCase		:     Optional[Any]=None      , **__lowerCAmelCase		:     Optional[Any]    ):
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					config_class
      _UpperCAmelCase         =					has_text_modality
      _UpperCAmelCase         =					kwargs
      _UpperCAmelCase         =					common_properties


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict    )
      _UpperCAmelCase         =					(
          ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
        common_properties.extend(["""vocab_size"""]    )

      # Test that config has the common properties as getters
      for prop in common_properties:
        self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )      , msg=f'''`{prop}` does not exist'''    )

      # Test that config has the common properties as setter
      for idx, name in enumerate(SCREAMING_SNAKE_CASE__    ):
        try:
          setattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )
          self.parent.assertEqual(
              getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )      , SCREAMING_SNAKE_CASE__      , msg=f'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )}'''    )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(SCREAMING_SNAKE_CASE__    ):
        try:
          _UpperCAmelCase         =					self.config_class(**{name: idx}    )
          self.parent.assertEqual(
              getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )      , SCREAMING_SNAKE_CASE__      , msg=f'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )}'''    )
        except NotImplementedError:
          # Some models might not be able to implement setters for common_properties
          # In that case, a NotImplementedError is raised
          pass


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict    )
      _UpperCAmelCase         =					json.loads(config.to_json_string()    )
      for key, value in self.inputs_dict.items():
        self.parent.assertEqual(obj[key]      , SCREAMING_SNAKE_CASE__    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict    )

      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase         =					os.path.join(SCREAMING_SNAKE_CASE__      , """config.json"""    )
        config_first.to_json_file(SCREAMING_SNAKE_CASE__    )
        _UpperCAmelCase         =					self.config_class.from_json_file(SCREAMING_SNAKE_CASE__    )

      self.parent.assertEqual(config_second.to_dict()      , config_first.to_dict()    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict    )

      with tempfile.TemporaryDirectory() as tmpdirname:
        config_first.save_pretrained(SCREAMING_SNAKE_CASE__    )
        _UpperCAmelCase         =					self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__    )

      self.parent.assertEqual(config_second.to_dict()      , config_first.to_dict()    )


    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict    )

      _UpperCAmelCase         =					"""test"""
      with tempfile.TemporaryDirectory() as tmpdirname:
        _UpperCAmelCase         =					os.path.join(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    )
        config_first.save_pretrained(SCREAMING_SNAKE_CASE__    )
        _UpperCAmelCase         =					self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__      , subfolder=SCREAMING_SNAKE_CASE__    )

      self.parent.assertEqual(config_second.to_dict()      , config_first.to_dict()    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.config_class(**self.inputs_dict      , num_labels=5    )
      self.parent.assertEqual(len(config.idalabel    )      , 5    )
      self.parent.assertEqual(len(config.labelaid    )      , 5    )

      _UpperCAmelCase         =					3
      self.parent.assertEqual(len(config.idalabel    )      , 3    )
      self.parent.assertEqual(len(config.labelaid    )      , 3    )


    def    lowerCAmelCase_     (    self		:     str    ):




      if self.config_class.is_composition:
        return
      _UpperCAmelCase         =					self.config_class()
      self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					copy.deepcopy(SCREAMING_SNAKE_CASE__    )
      _UpperCAmelCase         =					self.config_class(**SCREAMING_SNAKE_CASE__    )
      _UpperCAmelCase         =					[]
      for key, value in config_common_kwargs.items():
        if key == "torch_dtype":
          if not is_torch_available():
            continue
          else:
            import torch

            if config.torch_dtype != torch.floataa:
              wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)    )
        elif getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    ) != value:
          wrong_values.append((key, getattr(SCREAMING_SNAKE_CASE__      , SCREAMING_SNAKE_CASE__    ), value)    )

      if len(SCREAMING_SNAKE_CASE__    ) > 0:
        _UpperCAmelCase         =					"""\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]    )
        raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''    )


    def    lowerCAmelCase_     (    self		:     int    ):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()


"""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 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__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       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:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   XLMRobertaXLForSequenceClassification(_snake_case						) if classification_head else XLMRobertaXLForMaskedLM(_snake_case						)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE__						:       int           =   roberta_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.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]

														SCREAMING_SNAKE_CASE__						:       RobertaAttention           =   layer.attention
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.self_attn_layer_norm.bias

														# 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__						:       List[str]           =   roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__						:       int           =   roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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__						:       Union[str, Any]           =   roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   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__						:       Tuple           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.fca.bias
														# end of layer

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

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_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(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Union[str, 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.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




