import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      UpperCAmelCase_								=							"platform"
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class      lowerCAmelCase						:

     '''simple docstring'''





     _SCREAMING_SNAKE_CASE     :     Tuple		=PegasusConfig
     _SCREAMING_SNAKE_CASE     :     List[Any]		={}
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		="gelu"



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=13				,      lowerCAmelCase__=7				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False				,      lowerCAmelCase__=99				,      lowerCAmelCase__=32				,      lowerCAmelCase__=5				,      lowerCAmelCase__=4				,      lowerCAmelCase__=37				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=20				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      ):
            _A=	parent
            _A=	batch_size
            _A=	seq_length
            _A=	is_training
            _A=	use_labels
            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size

            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	eos_token_id
            _A=	pad_token_id
            _A=	bos_token_id



     def 			a__  (					self       ):
            _A=	ids_tensor([self.batch_size, self.seq_length - 1]				,      self.vocab_size       ).clip(3				,      self.vocab_size       )
            _A=	np.expand_dims(np.array([self.eos_token_id] * self.batch_size       )				,      1       )
            _A=	np.concatenate([input_ids, eos_tensor]				,      axis=1       )

            _A=	ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size       )

            _A=	self.config_cls(
                vocab_size=self.vocab_size				,      d_model=self.hidden_size				,      encoder_layers=self.num_hidden_layers				,      decoder_layers=self.num_hidden_layers				,      encoder_attention_heads=self.num_attention_heads				,      decoder_attention_heads=self.num_attention_heads				,      encoder_ffn_dim=self.intermediate_size				,      decoder_ffn_dim=self.intermediate_size				,      dropout=self.hidden_dropout_prob				,      attention_dropout=self.attention_probs_dropout_prob				,      max_position_embeddings=self.max_position_embeddings				,      eos_token_ids=[2]				,      bos_token_id=self.bos_token_id				,      pad_token_id=self.pad_token_id				,      decoder_start_token_id=self.pad_token_id				,      **self.config_updates				,      )
            _A=	prepare_pegasus_inputs_dict(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__       )
            return config, inputs_dict



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	20
            _A=	model_class_name(lowerCamelCase__       )

            _A=	model.encode(inputs_dict['input_ids']       )

            _A=	(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _A=	model.init_cache(decoder_input_ids.shape[0]				,      lowerCamelCase__				,      lowerCamelCase__       )
            _A=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,      dtype='i4'       )

            _A=	jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]				,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,      )
            _A=	model.decode(
                decoder_input_ids[:, :-1]				,      lowerCamelCase__				,      decoder_attention_mask=lowerCamelCase__				,      past_key_values=lowerCamelCase__				,      decoder_position_ids=lowerCamelCase__				,      )

            _A=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,      dtype='i4'       )
            _A=	model.decode(
                decoder_input_ids[:, -1:]				,      lowerCamelCase__				,      decoder_attention_mask=lowerCamelCase__				,      past_key_values=outputs_cache.past_key_values				,      decoder_position_ids=lowerCamelCase__				,      )

            _A=	model.decode(lowerCamelCase__				,      lowerCamelCase__       )

            _A=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
            self.parent.assertTrue(diff < 1E-3				,      msg=f"Max diff is {diff}"       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	20
            _A=	model_class_name(lowerCamelCase__       )

            _A=	model.encode(inputs_dict['input_ids']       )

            _A=	(
                inputs_dict['''decoder_input_ids'''],
                inputs_dict['''decoder_attention_mask'''],
            )

            _A=	jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
                ]				,      axis=-1				,      )

            _A=	model.init_cache(decoder_input_ids.shape[0]				,      lowerCamelCase__				,      lowerCamelCase__       )
            _A=	jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]				,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,      )

            _A=	model.decode(
                decoder_input_ids[:, :-1]				,      lowerCamelCase__				,      decoder_attention_mask=lowerCamelCase__				,      past_key_values=lowerCamelCase__				,      decoder_position_ids=lowerCamelCase__				,      )
            _A=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,      dtype='i4'       )
            _A=	model.decode(
                decoder_input_ids[:, -1:]				,      lowerCamelCase__				,      past_key_values=outputs_cache.past_key_values				,      decoder_attention_mask=lowerCamelCase__				,      decoder_position_ids=lowerCamelCase__				,      )

            _A=	model.decode(lowerCamelCase__				,      lowerCamelCase__				,      decoder_attention_mask=lowerCamelCase__       )

            _A=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
            self.parent.assertTrue(diff < 1E-3				,      msg=f"Max diff is {diff}"       )


def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=None       ,					)		->						Dict:




       '''simple docstring'''






       if attention_mask is None:
              _A=	np.not_equal(lowerCAmelCase_       ,					config.pad_token_id   ).astype(np.inta   )
       if decoder_attention_mask is None:
              _A=	np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape       ,					dtype=np.inta   ),
                      np.not_equal(decoder_input_ids[:, 1:]       ,					config.pad_token_id   ).astype(np.inta   ),
                  ]       ,					axis=-1       ,					)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }





@require_flax
class      lowerCAmelCase						(					__lowerCAmelCase			,		unittest.TestCase			):

     '''simple docstring'''





     _SCREAMING_SNAKE_CASE     :     Dict		=(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE     :     Any		=(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     _SCREAMING_SNAKE_CASE     :     Any		=True
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		=False
     _SCREAMING_SNAKE_CASE     :     Tuple		=False
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     def 			a__  (					self       ):
            _A=	FlaxPegasusModelTester(self       )
            _A=	ConfigTester(self				,      config_class=lowerCamelCase__       )



     def 			a__  (					self       ):
            self.config_tester.run_common_tests()



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__       ):
                          _A=	self._prepare_for_class(lowerCamelCase__				,      lowerCamelCase__       )
                          _A=	model_class(lowerCamelCase__       )

                          @jax.jit
                          def encode_jitted(lowerCAmelCase__				,      lowerCAmelCase__=None				,      **lowerCAmelCase__       ):
                                 return model.encode(input_ids=lowerCamelCase__				,      attention_mask=lowerCamelCase__       )

                          with self.subTest('JIT Enabled'       ):
                                 _A=	encode_jitted(**lowerCamelCase__       ).to_tuple()

                          with self.subTest('JIT Disabled'       ):
                                 with jax.disable_jit():
                                        _A=	encode_jitted(**lowerCamelCase__       ).to_tuple()

                          self.assertEqual(len(lowerCamelCase__       )				,      len(lowerCamelCase__       )       )
                          for jitted_output, output in zip(lowerCamelCase__				,      lowerCamelCase__       ):
                                 self.assertEqual(jitted_output.shape				,      output.shape       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__       ):
                          _A=	model_class(lowerCamelCase__       )
                          _A=	model.encode(inputs_dict['input_ids']				,      inputs_dict['attention_mask']       )

                          _A=	{
                              '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                              '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                              '''encoder_outputs''': encoder_outputs,
                          }

                          @jax.jit
                          def decode_jitted(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
                                 return model.decode(
                                     decoder_input_ids=lowerCamelCase__				,      decoder_attention_mask=lowerCamelCase__				,      encoder_outputs=lowerCamelCase__				,      )

                          with self.subTest('JIT Enabled'       ):
                                 _A=	decode_jitted(**lowerCamelCase__       ).to_tuple()

                          with self.subTest('JIT Disabled'       ):
                                 with jax.disable_jit():
                                        _A=	decode_jitted(**lowerCamelCase__       ).to_tuple()

                          self.assertEqual(len(lowerCamelCase__       )				,      len(lowerCamelCase__       )       )
                          for jitted_output, output in zip(lowerCamelCase__				,      lowerCamelCase__       ):
                                 self.assertEqual(jitted_output.shape				,      output.shape       )



     @slow
     def 			a__  (					self       ):






            for model_class_name in self.all_model_classes:
                   _A=	model_class_name.from_pretrained('google/pegasus-large'				,      from_pt=lowerCamelCase__       )
                   _A=	np.ones((1, 1)       )
                   _A=	model(lowerCamelCase__       )
                   self.assertIsNotNone(lowerCamelCase__       )





     @slow
     def 			a__  (					self       ):
            _A=	FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'       )
            _A=	PegasusTokenizer.from_pretrained('google/pegasus-xsum'       )

            _A=	[
                ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
                ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
            ]

            _A=	[
                '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
                '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
            ]

            _A=	tokenizer(lowerCamelCase__				,      return_tensors='np'				,      truncation=lowerCamelCase__				,      max_length=512				,      padding=lowerCamelCase__       )
            _A=	model.generate(**lowerCamelCase__				,      num_beams=2       ).sequences
            _A=	tokenizer.batch_decode(lowerCamelCase__				,      skip_special_tokens=lowerCamelCase__       )
            assert tgt_text == decoded
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__       )}       )