



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						):







     def  _lowercase			(       self      :     Optional[int]):
           A__	:							List[str]						    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
           A__	:							Optional[Any]						    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(_A)
           A__	:							str						    = -1

           A__	:							Optional[int]						    = ids_tensor((1, 5)			,		vocab_size=model.config.vocab_size).to(_A)
           A__	:							Tuple						    = model.generate(_A			,		max_new_tokens=10			,		do_sample=_A)
           A__	:							List[Any]						    = tokenizer.decode(greedy_ids[0])

           with CaptureStdout() as cs:
                 A__	:							List[str]						    = 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
           A__	:							Union[str, Any]						    = cs.out[:-1]

           self.assertEqual(_A			,		_A)







     def  _lowercase			(       self      :     Union[str, Any]):
           A__	:							Any						    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
           A__	:							str						    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(_A)
           A__	:							List[str]						    = -1

           A__	:							Tuple						    = ids_tensor((1, 5)			,		vocab_size=model.config.vocab_size).to(_A)
           A__	:							Tuple						    = model.generate(_A			,		max_new_tokens=10			,		do_sample=_A)
           A__	:							Any						    = tokenizer.decode(greedy_ids[0])

           A__	:							Any						    = TextIteratorStreamer(_A)
           A__	:							Optional[int]						    = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           A__	:							List[Any]						    = Thread(target=model.generate			,		kwargs=_A)
           thread.start()
           A__	:							List[Any]						    = ""
           for new_text in streamer:
                 streamer_text += new_text

           self.assertEqual(_A			,		_A)







     def  _lowercase			(       self      :     Tuple):
           A__	:							List[Any]						    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
           A__	:							List[Any]						    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(_A)
           A__	:							List[Any]						    = -1

           A__	:							List[Any]						    = ids_tensor((1, 5)			,		vocab_size=model.config.vocab_size).to(_A)
           A__	:							Tuple						    = model.generate(_A			,		max_new_tokens=10			,		do_sample=_A)
           A__	:							int						    = greedy_ids[:, input_ids.shape[1] :]
           A__	:							Optional[Any]						    = tokenizer.decode(new_greedy_ids[0])

           with CaptureStdout() as cs:
                 A__	:							Tuple						    = 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
           A__	:							Optional[Any]						    = cs.out[:-1]

           self.assertEqual(_A			,		_A)







     def  _lowercase			(       self      :     Dict):
           # 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
           A__	:							Union[str, Any]						    = AutoTokenizer.from_pretrained("distilgpt2")
           A__	:							Dict						    = AutoModelForCausalLM.from_pretrained("distilgpt2").to(_A)
           A__	:							List[Any]						    = -1

           A__	:							Union[str, Any]						    = torch.ones((1, 5)			,		device=_A).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
                 A__	:							int						    = 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
           A__	:							Optional[int]						    = cs.out[:-1]  # Remove the final "\n"
           A__	:							Any						    = tokenizer(_A			,		return_tensors="pt")
           self.assertEqual(streamer_text_tokenized.input_ids.shape			,		(1, 1))







     def  _lowercase			(       self      :     List[str]):
           A__	:							Optional[int]						    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
           A__	:							Dict						    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(_A)
           A__	:							Optional[int]						    = -1

           A__	:							List[Any]						    = ids_tensor((1, 5)			,		vocab_size=model.config.vocab_size).to(_A)
           A__	:							Dict						    = TextIteratorStreamer(_A			,		timeout=0.0_01)
           A__	:							Dict						    = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
           A__	:							Tuple						    = 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):
                 A__	:							List[Any]						    = ""
                 for new_text in streamer:
                       streamer_text += new_text




from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt
def 	snake_case__       (					__lowercase       )		->		bool:







						"""simple docstring"""

						A__	:							int						    = int(number**0.5       )
						return number == sq * sq
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		tuple[int, int]:







						"""simple docstring"""

						A__	:							int						    = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
						A__	:							int						    = x_den * y_den * z_den
						A__	:							int						    = gcd(__lowercase							,     __lowercase       )
						top //= hcf
						bottom //= hcf
						return top, bottom
def 	snake_case__       (					__lowercase = 3_5       )		->		int:







						"""simple docstring"""

						A__	:							set						    = set()
						A__	:							int
						A__	:							Fraction						    = Fraction(0       )
						A__	:							tuple[int, int]

						for x_num in range(1							,     order + 1       ):
												for x_den in range(x_num + 1							,     order + 1       ):
																		for y_num in range(1							,     order + 1       ):
																								for y_den in range(y_num + 1							,     order + 1       ):
																														# n=1
																														A__	:							Any						    = x_num * y_den + x_den * y_num
																														A__	:							List[Any]						    = x_den * y_den
																														A__	:							List[Any]						    = gcd(__lowercase							,     __lowercase       )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																				A__	:							List[Any]						    = add_three(
																																				    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																				unique_s.add(__lowercase       )

																														# n=2
																														A__	:							Any						    = (
																														    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																														)
																														A__	:							Optional[int]						    = x_den * x_den * y_den * y_den
																														if is_sq(__lowercase       ) and is_sq(__lowercase       ):
																																				A__	:							Union[str, Any]						    = int(sqrt(__lowercase       )       )
																																				A__	:							int						    = int(sqrt(__lowercase       )       )
																																				A__	:							Any						    = gcd(__lowercase							,     __lowercase       )
																																				z_num //= hcf
																																				z_den //= hcf
																																				if 0 < z_num < z_den <= order:
																																										A__	:							List[Any]						    = add_three(
																																										    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																										unique_s.add(__lowercase       )

                    # n=-1
																														A__	:							Tuple						    = x_num * y_num
																														A__	:							int						    = x_den * y_num + x_num * y_den
																														A__	:							List[str]						    = gcd(__lowercase							,     __lowercase       )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																				A__	:							str						    = add_three(
																																				    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																				unique_s.add(__lowercase       )

																														# n=2
																														A__	:							Any						    = x_num * x_num * y_num * y_num
																														A__	:							List[str]						    = (
																														    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																														)
																														if is_sq(__lowercase       ) and is_sq(__lowercase       ):
																																				A__	:							Optional[int]						    = int(sqrt(__lowercase       )       )
																																				A__	:							List[Any]						    = int(sqrt(__lowercase       )       )
																																				A__	:							Union[str, Any]						    = gcd(__lowercase							,     __lowercase       )
																																				z_num //= hcf
																																				z_den //= hcf
																																				if 0 < z_num < z_den <= order:
																																										A__	:							Optional[Any]						    = add_three(
																																										    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )
																																										unique_s.add(__lowercase       )

						for num, den in unique_s:
												total += Fraction(__lowercase							,     __lowercase       )

						return total.denominator + total.numerator


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