import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase_								=							logging.get_logger(__name__)
logging.set_verbosity_info()

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       if "xprophetnet" in prophetnet_checkpoint_path:
              _A=	XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_   )
              _A,			_A=	XLMProphetNetForConditionalGeneration.from_pretrained(
                  lowerCAmelCase_       ,					output_loading_info=lowerCAmelCase_   )
       else:
              _A=	ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_   )
              _A,			_A=	ProphetNetForConditionalGeneration.from_pretrained(
                  lowerCAmelCase_       ,					output_loading_info=lowerCAmelCase_   )

       _A=	['key_proj', 'value_proj', 'query_proj']

       _A=	{
           'self_attn': 'ngram_self_attn',
           'cross_attn': 'encoder_attn',
           'cross_attn_layer_norm': 'encoder_attn_layer_norm',
           'feed_forward_layer_norm': 'final_layer_norm',
           'feed_forward': '',
           'intermediate': 'fc1',
           'output': 'fc2',
           'key_proj': 'k_proj',
           'query_proj': 'q_proj',
           'value_proj': 'v_proj',
           'word_embeddings': 'embed_tokens',
           'embeddings_layer_norm': 'emb_layer_norm',
           'relative_pos_embeddings': 'relative_linear',
           'ngram_embeddings': 'ngram_input_embed',
           'position_embeddings': 'embed_positions',
       }

       for key in loading_info["missing_keys"]:
              _A=	key.split('.'   )

              if attributes[0] == "lm_head":
                     _A=	prophet
                     _A=	prophet_old
              else:
                     _A=	prophet.prophetnet
                     _A=	prophet_old.model

              _A=	False
              for attribute in attributes:
                     if attribute in mapping:
                            _A=	mapping[attribute]
                            if not hasattr(lowerCAmelCase_       ,					lowerCAmelCase_   ) and len(lowerCAmelCase_   ) > 0:
                                   _A=	attribute
                     elif hasattr(lowerCAmelCase_       ,					lowerCAmelCase_   ):
                            _A=	attribute

                     if attribute == "weight":
                            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                            _A=	old_model.weight
                            logger.info(F"{attribute} is initialized."   )
                            _A=	True
                            break
                     elif attribute == "bias":
                            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                            _A=	old_model.bias
                            logger.info(F"{attribute} is initialized"   )
                            _A=	True
                            break
                     elif attribute in special_keys and hasattr(lowerCAmelCase_       ,					'in_proj_weight'   ):
                            _A=	old_model.in_proj_weight.shape[0] // 3
                            _A=	getattr(lowerCAmelCase_       ,					lowerCAmelCase_   )
                            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                            if attribute == "query_proj":
                                   _A=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
                                   _A=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

                            elif attribute == "key_proj":
                                   _A=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
                                   _A=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
                            elif attribute == "value_proj":
                                   _A=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
                                   _A=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
                            _A=	True
                            break
                     elif attribute == "position_embeddings":
                            assert (
                                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                            ), "Hidden size has to match"
                            assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
                            _A=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
                            _A=	True
                            break

                     if attribute.isdigit():
                            _A=	model[int(lowerCAmelCase_   )]
                            _A=	old_model[int(lowerCAmelCase_   )]
                     else:
                            _A=	getattr(lowerCAmelCase_       ,					lowerCAmelCase_   )

                            if old_attribute == "":
                                   _A=	old_model
                            else:
                                   if not hasattr(lowerCAmelCase_       ,					lowerCAmelCase_   ):
                                          raise ValueError(F"{old_model} does not have {old_attribute}"   )
                                   _A=	getattr(lowerCAmelCase_       ,					lowerCAmelCase_   )

              if not is_key_init:
                     raise ValueError(F"{key} was not correctly initialized!"   )

       print(F"Saving model to {pytorch_dump_folder_path}"   )
       prophet.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--prophetnet_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.'''
      )
      UpperCAmelCase_								=							parser.parse_args()
      convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
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 							UpperCamelCase				(    *lowerCAmelCase_       ,					**lowerCAmelCase_   )		->						List[Any]:




             '''simple docstring'''






             pass





      UpperCAmelCase_								=							False


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

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	pipeline(
           task=args.task       ,					model=args.model if args.model else None       ,					config=args.config       ,					tokenizer=args.tokenizer       ,					device=args.device       ,					)
       return ServeCommand(lowerCAmelCase_       ,					args.host       ,					args.port       ,					args.workers   )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     dict





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]
     _SCREAMING_SNAKE_CASE     :     Optional[List[int]]





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     str





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any





class      lowerCAmelCase						(					_a			):



     @staticmethod
     def 			a__  (					lowerCAmelCase__       ):
            _A=	parser.add_parser(
                'serve'				,      help='CLI tool to run inference requests through REST and GraphQL endpoints.'       )
            serve_parser.add_argument(
                '--task'				,      type=lowerCAmelCase__				,      choices=get_supported_tasks()				,      help='The task to run the pipeline on'				,      )
            serve_parser.add_argument('--host'				,      type=lowerCAmelCase__				,      default='localhost'				,      help='Interface the server will listen on.'       )
            serve_parser.add_argument('--port'				,      type=lowerCAmelCase__				,      default=8888				,      help='Port the serving will listen to.'       )
            serve_parser.add_argument('--workers'				,      type=lowerCAmelCase__				,      default=1				,      help='Number of http workers'       )
            serve_parser.add_argument('--model'				,      type=lowerCAmelCase__				,      help='Model\'s name or path to stored model.'       )
            serve_parser.add_argument('--config'				,      type=lowerCAmelCase__				,      help='Model\'s config name or path to stored model.'       )
            serve_parser.add_argument('--tokenizer'				,      type=lowerCAmelCase__				,      help='Tokenizer name to use.'       )
            serve_parser.add_argument(
                '--device'				,      type=lowerCAmelCase__				,      default=-1				,      help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'				,      )
            serve_parser.set_defaults(func=lowerCAmelCase__       )



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	pipeline

            _A=	host
            _A=	port
            _A=	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}"       )
                   _A=	FastAPI(
                       routes=[
                           APIRoute(
                               '/'				,      self.model_info				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['GET']				,      ),
                           APIRoute(
                               '/tokenize'				,      self.tokenize				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                           APIRoute(
                               '/detokenize'				,      self.detokenize				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                           APIRoute(
                               '/forward'				,      self.forward				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                       ]				,      timeout=600				,      )



     def 			a__  (					self       ):
            run(self._app				,      host=self.host				,      port=self.port				,      workers=self.workers       )



     def 			a__  (					self       ):
            return ServeModelInfoResult(infos=vars(self._pipeline.model.config       )       )



     def 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):
            try:
                   _A=	self._pipeline.tokenizer.tokenize(lowerCAmelCase__       )

                   if return_ids:
                          _A=	self._pipeline.tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )
                          return ServeTokenizeResult(tokens=lowerCAmelCase__				,      tokens_ids=lowerCAmelCase__       )
                   else:
                          return ServeTokenizeResult(tokens=lowerCAmelCase__       )

            except Exception as e:
                   raise HTTPException(status_code=500				,      detail={'model': '', 'error': str(lowerCAmelCase__       )}       )



     def 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      ):
            try:
                   _A=	self._pipeline.tokenizer.decode(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
                   return ServeDeTokenizeResult(model=''				,      text=lowerCAmelCase__       )
            except Exception as e:
                   raise HTTPException(status_code=500				,      detail={'model': '', 'error': str(lowerCAmelCase__       )}       )





     async def 			a__  (					self				,      lowerCAmelCase__=Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):

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

            try:
                   # Forward through the model
                   _A=	self._pipeline(lowerCAmelCase__       )
                   return ServeForwardResult(output=lowerCAmelCase__       )
            except Exception as e:
                   raise HTTPException(500				,      {'error': str(lowerCAmelCase__       )}       )