





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								=							logging.get_logger(__name__)

snake_case								=							"""Hello world! cécé herlolip"""

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str      ,				snake_case__	:str      ,				snake_case__	:bool  )   ->    Any:
  _lowercase						    =			FairseqRobertaModel.from_pretrained(snake_case__  )
  roberta.eval()  # disable dropout
  _lowercase						    =			roberta.model.encoder.sentence_encoder
  _lowercase						    =			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:
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'      ,				snake_case__  )

  _lowercase						    =			XLMRobertaXLForSequenceClassification(snake_case__  ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__  )
  model.eval()

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

  _lowercase						    =			roberta_sent_encoder.layer_norm.weight
  _lowercase						    =			roberta_sent_encoder.layer_norm.bias

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

    _lowercase						    =			layer.attention
    _lowercase						    =			roberta_layer.self_attn_layer_norm.weight
    _lowercase						    =			roberta_layer.self_attn_layer_norm.bias

    # self attention
    _lowercase						    =			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)  )
    )

    _lowercase						    =			roberta_layer.self_attn.q_proj.weight
    _lowercase						    =			roberta_layer.self_attn.q_proj.bias
    _lowercase						    =			roberta_layer.self_attn.k_proj.weight
    _lowercase						    =			roberta_layer.self_attn.k_proj.bias
    _lowercase						    =			roberta_layer.self_attn.v_proj.weight
    _lowercase						    =			roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _lowercase						    =			layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _lowercase						    =			roberta_layer.self_attn.out_proj.weight
    _lowercase						    =			roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    _lowercase						    =			roberta_layer.final_layer_norm.weight
    _lowercase						    =			roberta_layer.final_layer_norm.bias

    # intermediate
    _lowercase						    =			layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase						    =			roberta_layer.fca.weight
    _lowercase						    =			roberta_layer.fca.bias

    # output
    _lowercase						    =			layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase						    =			roberta_layer.fca.weight
    _lowercase						    =			roberta_layer.fca.bias
    # end of layer

  if classification_head:
    _lowercase						    =			roberta.model.classification_heads['mnli'].dense.weight
    _lowercase						    =			roberta.model.classification_heads['mnli'].dense.bias
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.weight
    _lowercase						    =			roberta.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    _lowercase						    =			roberta.model.encoder.lm_head.dense.weight
    _lowercase						    =			roberta.model.encoder.lm_head.dense.bias
    _lowercase						    =			roberta.model.encoder.lm_head.layer_norm.weight
    _lowercase						    =			roberta.model.encoder.lm_head.layer_norm.bias
    _lowercase						    =			roberta.model.encoder.lm_head.weight
    _lowercase						    =			roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  _lowercase						    =			roberta.encode(snake_case__  ).unsqueeze(0  )  # batch of size 1

  _lowercase						    =			model(snake_case__  )[0]
  if classification_head:
    _lowercase						    =			roberta.model.classification_heads['mnli'](roberta.extract_features(snake_case__  )  )
  else:
    _lowercase						    =			roberta.model(snake_case__  )[0]
  print(our_output.shape      ,				their_output.shape  )
  _lowercase						    =			torch.max(torch.abs(our_output - their_output  )  ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
  _lowercase						    =			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__":
      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_xlm_roberta_xl_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )






from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from fastapi import Body, FastAPI, HTTPException
							from fastapi.routing import APIRoute
							from pydantic import BaseModel
							from starlette.responses import JSONResponse
							from uvicorn import run

							UpperCAmelCase_							=				True
except (ImportError, AttributeError):
							UpperCAmelCase_							=				object





							def  __magic_name__  (				*lowercase				,       **lowercase				)				-> Optional[Any]:

														"""simple docstring"""



														pass




							UpperCAmelCase_							=				False


UpperCAmelCase_							=				logging.get_logger("""transformers-cli/serving""")





def  __magic_name__  (				lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Any  				=     pipeline(
							    task=args.task				,       model=args.model if args.model else None				,       config=args.config				,       tokenizer=args.tokenizer				,       device=args.device				,       )
							return ServeCommand(lowercase				,       args.host				,       args.port				,       args.workers				)
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       dict




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[str]
		__a       :       Optional[List[int]]




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		@staticmethod
		def 						snake_case__				(							snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     parser.add_parser(
									    """serve""",   help="""CLI tool to run inference requests through REST and GraphQL endpoints."""  )
									serve_parser.add_argument(
									    """--task""",   type=snake_case__,   choices=get_supported_tasks(),   help="""The task to run the pipeline on""",   )
									serve_parser.add_argument("""--host""",   type=snake_case__,   default="""localhost""",   help="""Interface the server will listen on."""  )
									serve_parser.add_argument("""--port""",   type=snake_case__,   default=88_88,   help="""Port the serving will listen to."""  )
									serve_parser.add_argument("""--workers""",   type=snake_case__,   default=1,   help="""Number of http workers"""  )
									serve_parser.add_argument("""--model""",   type=snake_case__,   help="""Model's name or path to stored model."""  )
									serve_parser.add_argument("""--config""",   type=snake_case__,   help="""Model's config name or path to stored model."""  )
									serve_parser.add_argument("""--tokenizer""",   type=snake_case__,   help="""Tokenizer name to use."""  )
									serve_parser.add_argument(
									    """--device""",   type=snake_case__,   default=-1,   help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""",   )
									serve_parser.set_defaults(func=snake_case__  )






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   str  				=     pipeline

									lowercase_	:   List[str]  				=     host
									lowercase_	:   int  				=     port
									lowercase_	:   Optional[Any]  				=     workers

									if not _serve_dependencies_installed:
																raise RuntimeError(
																    """Using serve command requires FastAPI and uvicorn. """
																    """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
																    """Or install FastAPI and uvicorn separately."""  )
									else:
																logger.info(f"""Serving model over {host}:{port}"""  )
																lowercase_	:   Union[str, Any]  				=     FastAPI(
																    routes=[
																        APIRoute(
																            """/""",   self.model_info,   response_model=snake_case__,   response_class=snake_case__,   methods=["""GET"""],   ),
																        APIRoute(
																            """/tokenize""",   self.tokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/detokenize""",   self.detokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/forward""",   self.forward,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																    ],   timeout=6_00,   )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									run(self._app,   host=self.host,   port=self.port,   workers=self.workers  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  )  )       -> Optional[int]:






									"""simple docstring"""
									try:
																lowercase_	:   Tuple  				=     self._pipeline.tokenizer.tokenize(snake_case__  )

																if return_ids:
																							lowercase_	:   Union[str, Any]  				=     self._pipeline.tokenizer.convert_tokens_to_ids(snake_case__  )
																							return ServeTokenizeResult(tokens=snake_case__,   tokens_ids=snake_case__  )
																else:
																							return ServeTokenizeResult(tokens=snake_case__  )

									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   )       -> Dict:






									"""simple docstring"""
									try:
																lowercase_	:   List[Any]  				=     self._pipeline.tokenizer.decode(snake_case__,   snake_case__,   snake_case__  )
																return ServeDeTokenizeResult(model="""""",   text=snake_case__  )
									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		async def 						snake_case__				(							self,   snake_case__=Body(snake_case__,   embed=snake_case__  )  )       -> Tuple:






									"""simple docstring"""

									# Check we don't have empty string
									if len(snake_case__  ) == 0:
																return ServeForwardResult(output=[],   attention=[]  )

									try:
																# Forward through the model
																lowercase_	:   Optional[int]  				=     self._pipeline(snake_case__  )
																return ServeForwardResult(output=snake_case__  )
									except Exception as e:
																raise HTTPException(5_00,   {"""error""": str(snake_case__  )}  )