


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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size    ).to(A_    )
         __UpperCamelCase    							=model.generate(A_					,   max_new_tokens=10					,   do_sample=A_    )
         __UpperCamelCase    							=tokenizer.decode(greedy_ids[0]    )

         with CaptureStdout() as cs:
              __UpperCamelCase    							=TextStreamer(A_    )
              model.generate(A_					,   max_new_tokens=10					,   do_sample=A_					,   streamer=A_    )
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         __UpperCamelCase    							=cs.out[:-1]

         self.assertEqual(A_					,   A_    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size    ).to(A_    )
         __UpperCamelCase    							=model.generate(A_					,   max_new_tokens=10					,   do_sample=A_    )
         __UpperCamelCase    							=tokenizer.decode(greedy_ids[0]    )

         __UpperCamelCase    							=TextIteratorStreamer(A_    )
         __UpperCamelCase    							={'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
         __UpperCamelCase    							=Thread(target=model.generate					,   kwargs=A_    )
         thread.start()
         __UpperCamelCase    							=''
         for new_text in streamer:
              streamer_text += new_text

         self.assertEqual(A_					,   A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size    ).to(A_    )
         __UpperCamelCase    							=model.generate(A_					,   max_new_tokens=10					,   do_sample=A_    )
         __UpperCamelCase    							=greedy_ids[:, input_ids.shape[1] :]
         __UpperCamelCase    							=tokenizer.decode(new_greedy_ids[0]    )

         with CaptureStdout() as cs:
              __UpperCamelCase    							=TextStreamer(A_					,   skip_prompt=A_    )
              model.generate(A_					,   max_new_tokens=10					,   do_sample=A_					,   streamer=A_    )
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         __UpperCamelCase    							=cs.out[:-1]

         self.assertEqual(A_					,   A_    )






    def _a					(			self    )  ->       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    							=AutoTokenizer.from_pretrained('distilgpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('distilgpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=torch.ones((1, 5)					,   device=A_    ).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
              __UpperCamelCase    							=TextStreamer(A_					,   skip_special_tokens=A_    )
              model.generate(A_					,   max_new_tokens=1					,   do_sample=A_					,   streamer=A_    )

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






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
         __UpperCamelCase    							=AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'    ).to(A_    )
         __UpperCamelCase    							=-1

         __UpperCamelCase    							=ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size    ).to(A_    )
         __UpperCamelCase    							=TextIteratorStreamer(A_					,   timeout=0.001    )
         __UpperCamelCase    							={'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
         __UpperCamelCase    							=Thread(target=model.generate					,   kwargs=A_    )
         thread.start()

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









from __future__ import annotations

import math






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=size
         # approximate the overall size of segment tree with given value
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         # create array to store lazy update
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]  # flag for lazy update






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2 + 1






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       None:
         if left_element == right_element:
              __UpperCamelCase    							=a[left_element - 1]
         else:
              __UpperCamelCase    							=(left_element + right_element) // 2
              self.build(self.left(A_    )					,   A_					,   A_					,   A_    )
              self.build(self.right(A_    )					,   mid + 1					,   A_					,   A_    )
              __UpperCamelCase    							=max(
                  self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       bool:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True

         if right_element < a or left_element > b:
              return True
         if left_element >= a and right_element <= b:
              __UpperCamelCase    							=val
              if left_element != right_element:
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
              return True
         __UpperCamelCase    							=(left_element + right_element) // 2
         self.update(self.left(A_    )					,   A_					,   A_					,   A_					,   A_					,   A_    )
         self.update(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=max(
             self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )
         return True






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int | float:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
         if right_element < a or left_element > b:
              return -math.inf
         if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
         __UpperCamelCase    							=(left_element + right_element) // 2
         __UpperCamelCase    							=self.query(self.left(A_    )					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=self.query(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_    )
         return max(A_					,   A_    )






    def __str__(			self    )  ->       str:
         return str([self.query(1					,   1					,   self.size					,   A_					,   A_    ) for i in range(1					,   self.size + 1    )]    )


if __name__ == "__main__":
   _A		       =						[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A		       =						15
   _A		       =						SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 111)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 235)
   print(segt)





