



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers import AutoModelForCausalLM



@require_torch
class 						lowercase__      (unittest.TestCase						):

     """simple docstring"""






     def 		lowercase   ( self :	List[Any] ):
      snake_case__				:							int      							= AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
      snake_case__				:							Union[str, Any]      							= AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__UpperCamelCase )
      snake_case__				:							Tuple      							= -1

      snake_case__				:							int      							= ids_tensor((1, 5)   ,			vocab_size=model.config.vocab_size ).to(__UpperCamelCase )
      snake_case__				:							Dict      							= model.generate(__UpperCamelCase   ,			max_new_tokens=1_0   ,			do_sample=__UpperCamelCase )
      snake_case__				:							Optional[Any]      							= tokenizer.decode(greedy_ids[0] )

      with CaptureStdout() as cs:
       snake_case__				:							int      							= TextStreamer(__UpperCamelCase )
       model.generate(__UpperCamelCase   ,			max_new_tokens=1_0   ,			do_sample=__UpperCamelCase   ,			streamer=__UpperCamelCase )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      snake_case__				:							Union[str, Any]      							= cs.out[:-1]

      self.assertEqual(__UpperCamelCase   ,			__UpperCamelCase )


     def 		lowercase   ( self :	int ):
      snake_case__				:							Optional[int]      							= AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
      snake_case__				:							Dict      							= AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__UpperCamelCase )
      snake_case__				:							int      							= -1

      snake_case__				:							Union[str, Any]      							= ids_tensor((1, 5)   ,			vocab_size=model.config.vocab_size ).to(__UpperCamelCase )
      snake_case__				:							List[Any]      							= model.generate(__UpperCamelCase   ,			max_new_tokens=1_0   ,			do_sample=__UpperCamelCase )
      snake_case__				:							Any      							= tokenizer.decode(greedy_ids[0] )

      snake_case__				:							Any      							= TextIteratorStreamer(__UpperCamelCase )
      snake_case__				:							List[str]      							= {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
      snake_case__				:							List[str]      							= Thread(target=model.generate   ,			kwargs=__UpperCamelCase )
      thread.start()
      snake_case__				:							int      							= """"""
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(__UpperCamelCase   ,			__UpperCamelCase )


     def 		lowercase   ( self :	int ):
      snake_case__				:							str      							= AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
      snake_case__				:							Any      							= AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__UpperCamelCase )
      snake_case__				:							Tuple      							= -1

      snake_case__				:							Any      							= ids_tensor((1, 5)   ,			vocab_size=model.config.vocab_size ).to(__UpperCamelCase )
      snake_case__				:							str      							= model.generate(__UpperCamelCase   ,			max_new_tokens=1_0   ,			do_sample=__UpperCamelCase )
      snake_case__				:							Optional[Any]      							= greedy_ids[:, input_ids.shape[1] :]
      snake_case__				:							Tuple      							= tokenizer.decode(new_greedy_ids[0] )

      with CaptureStdout() as cs:
       snake_case__				:							str      							= TextStreamer(__UpperCamelCase   ,			skip_prompt=__UpperCamelCase )
       model.generate(__UpperCamelCase   ,			max_new_tokens=1_0   ,			do_sample=__UpperCamelCase   ,			streamer=__UpperCamelCase )
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      snake_case__				:							List[Any]      							= cs.out[:-1]

      self.assertEqual(__UpperCamelCase   ,			__UpperCamelCase )


     def 		lowercase   ( self :	List[Any] ):
      # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
      # with actual models -- the dummy models' tokenizers are not aligned with their models, and
      # `skip_special_tokens=True` has no effect on them
      snake_case__				:							List[str]      							= AutoTokenizer.from_pretrained("""distilgpt2""" )
      snake_case__				:							Tuple      							= AutoModelForCausalLM.from_pretrained("""distilgpt2""" ).to(__UpperCamelCase )
      snake_case__				:							Any      							= -1

      snake_case__				:							Dict      							= torch.ones((1, 5)   ,			device=__UpperCamelCase ).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
       snake_case__				:							List[str]      							= TextStreamer(__UpperCamelCase   ,			skip_special_tokens=__UpperCamelCase )
       model.generate(__UpperCamelCase   ,			max_new_tokens=1   ,			do_sample=__UpperCamelCase   ,			streamer=__UpperCamelCase )

      # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
      # re-tokenized, must only contain one token
      snake_case__				:							List[Any]      							= cs.out[:-1]  # Remove the final "\n"
      snake_case__				:							List[str]      							= tokenizer(__UpperCamelCase   ,			return_tensors="""pt""" )
      self.assertEqual(streamer_text_tokenized.input_ids.shape   ,			(1, 1) )


     def 		lowercase   ( self :	Any ):
      snake_case__				:							List[Any]      							= AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
      snake_case__				:							str      							= AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(__UpperCamelCase )
      snake_case__				:							Any      							= -1

      snake_case__				:							Any      							= ids_tensor((1, 5)   ,			vocab_size=model.config.vocab_size ).to(__UpperCamelCase )
      snake_case__				:							str      							= TextIteratorStreamer(__UpperCamelCase   ,			timeout=0.001 )
      snake_case__				:							Any      							= {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
      snake_case__				:							int      							= Thread(target=model.generate   ,			kwargs=__UpperCamelCase )
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(__UpperCamelCase ):
       snake_case__				:							str      							= """"""
       for new_text in streamer:
        streamer_text += new_text









import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))