
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 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 def      _lowerCamelCase       (      self	:Any		)	->				Dict:
        __UpperCamelCase			:     Union[str, Any]         =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
        __UpperCamelCase			:     Dict         =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(SCREAMING_SNAKE_CASE_		)
        __UpperCamelCase			:     List[str]         =      -1

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

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

        self.assertEqual(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_		)







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

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

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

        self.assertEqual(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_		)







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

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

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

        self.assertEqual(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_		)







 def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				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
        __UpperCamelCase			:     Optional[int]         =      AutoTokenizer.from_pretrained("distilgpt2"		)
        __UpperCamelCase			:     List[str]         =      AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(SCREAMING_SNAKE_CASE_		)
        __UpperCamelCase			:     Optional[int]         =      -1

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

        # 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
        __UpperCamelCase			:     Any         =      cs.out[:-1]  # Remove the final "\n"
        __UpperCamelCase			:     Union[str, Any]         =      tokenizer(SCREAMING_SNAKE_CASE_  ,     return_tensors="pt"		)
        self.assertEqual(streamer_text_tokenized.input_ids.shape  ,     (1, 1)		)







 def      _lowerCamelCase       (      self	:Any		)	->				Tuple:
        __UpperCamelCase			:     Dict         =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
        __UpperCamelCase			:     Any         =      AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(SCREAMING_SNAKE_CASE_		)
        __UpperCamelCase			:     Dict         =      -1

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

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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int | None, int | None, float]:






							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							__UpperCamelCase			:     Tuple         =      (low + high) // 2
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_subarray(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      max_subarray(_lowerCamelCase		,						mid + 1		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_cross_sum(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int, int, float]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      float("-inf"), -1
							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      float("-inf"), -1

							__UpperCamelCase			:     int | float         =      0
							for i in range(_lowerCamelCase		,						low - 1		,						-1):
														summ += arr[i]
														if summ > left_sum:
																					__UpperCamelCase			:     Tuple         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							__UpperCamelCase			:     Union[str, Any]         =      0
							for i in range(mid + 1		,						high + 1):
														summ += arr[i]
														if summ > right_sum:
																					__UpperCamelCase			:     Union[str, Any]         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							return max_left, max_right, (left_sum + right_sum)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      [randint(1		,						_lowerCamelCase) for _ in range(_lowerCamelCase)]
							__UpperCamelCase			:     Optional[Any]         =      time.time()
							max_subarray(_lowerCamelCase		,						0		,						input_size - 1)
							__UpperCamelCase			:     Any         =      time.time()
							return end - start




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
							__UpperCamelCase			:     Union[str, Any]         =      [time_max_subarray(_lowerCamelCase) for input_size in input_sizes]
							print("No of Inputs\t\tTime Taken")
							for input_size, runtime in zip(_lowerCamelCase		,						_lowerCamelCase):
														print(_lowerCamelCase		,						"\t\t"		,						_lowerCamelCase)
							plt.plot(_lowerCamelCase		,						_lowerCamelCase)
							plt.xlabel("Number of Inputs")
							plt.ylabel("Time taken in seconds")
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()