


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   ):




							"""simple docstring"""


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

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

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

														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Union[str, Any]:
														__snake_case	:    Dict											=     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
														__snake_case	:    Dict											=     AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(lowerCamelCase       )
														__snake_case	:    int											=     -1

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

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

														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"       )
														__snake_case	:    Tuple											=     AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"       ).to(lowerCamelCase       )
														__snake_case	:    str											=     -1

														__snake_case	:    Union[str, Any]											=     ids_tensor((1, 5)					,			vocab_size=model.config.vocab_size       ).to(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     model.generate(lowerCamelCase					,			max_new_tokens=10					,			do_sample=lowerCamelCase       )
														__snake_case	:    Optional[int]											=     greedy_ids[:, input_ids.shape[1] :]
														__snake_case	:    str											=     tokenizer.decode(new_greedy_ids[0]       )

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

														self.assertEqual(lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Dict       )			->					Tuple:
														# 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	:    int											=     AutoTokenizer.from_pretrained("distilgpt2"       )
														__snake_case	:    Optional[Any]											=     AutoModelForCausalLM.from_pretrained("distilgpt2"       ).to(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     -1

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

														# 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	:    Optional[int]											=     tokenizer(lowerCamelCase					,			return_tensors="pt"       )
														self.assertEqual(streamer_text_tokenized.input_ids.shape					,			(1, 1)       )






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

														__snake_case	:    Tuple											=     ids_tensor((1, 5)					,			vocab_size=model.config.vocab_size       ).to(lowerCamelCase       )
														__snake_case	:    Any											=     TextIteratorStreamer(lowerCamelCase					,			timeout=0.0_01       )
														__snake_case	:    Any											=     {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
														__snake_case	:    Tuple											=     Thread(target=model.generate					,			kwargs=lowerCamelCase       )
														thread.start()

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





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							if initial_intensity < 0:
														raise ValueError("The value of intensity cannot be negative"			)
														# handling of negative values of initial intensity
							if angle < 0 or angle > 3_6_0:
														raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"			)
														# handling of values out of allowed range
							return initial_intensity * (math.cos(math.radians(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

