
"""simple docstring"""


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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

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

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

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

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

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

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

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

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

							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							# 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
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''distilgpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''distilgpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

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

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





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   )
							lowerCamelCase_					=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  -1

							lowerCamelCase_					=  ids_tensor((1, 5)							,						vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  TextIteratorStreamer(UpperCAmelCase							,						timeout=0.0_0_1   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
							lowerCamelCase_					=  Thread(target=model.generate							,						kwargs=UpperCAmelCase   )
							thread.start()

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




from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






