



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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

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

           with CaptureStdout() as cs:
               __a							=    TextStreamer(__SCREAMING_SNAKE_CASE)
               model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=10					,		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
           __a							=    cs.out[:-1]

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

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

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

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

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

           with CaptureStdout() as cs:
               __a							=    TextStreamer(__SCREAMING_SNAKE_CASE					,		skip_prompt=__SCREAMING_SNAKE_CASE)
               model.generate(__SCREAMING_SNAKE_CASE					,		max_new_tokens=10					,		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
           __a							=    cs.out[:-1]

           self.assertEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''distilgpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''distilgpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    torch.ones((1, 5)					,		device=__SCREAMING_SNAKE_CASE).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
               __a							=    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
           __a							=    cs.out[:-1]  # Remove the final "\n"
           __a							=    tokenizer(__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''')
           self.assertEqual(streamer_text_tokenized.input_ids.shape					,		(1, 1))





       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''')
           __a							=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''').to(__SCREAMING_SNAKE_CASE)
           __a							=    -1

           __a							=    ids_tensor((1, 5)					,		vocab_size=model.config.vocab_size).to(__SCREAMING_SNAKE_CASE)
           __a							=    TextIteratorStreamer(__SCREAMING_SNAKE_CASE					,		timeout=0.0_01)
           __a							=    {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
           __a							=    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):
               __a							=    ''''''
               for new_text in streamer:
                   streamer_text += new_text










def 					__snake_case     (      _UpperCAmelCase = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


if __name__ == "__main__":
 print(f'{solution() = }')





