




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 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			)
								_UpperCAmelCase      =			AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			-1

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

								with CaptureStdout() as cs:
														_UpperCAmelCase      =			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
								_UpperCAmelCase      =			cs.out[:-1]

								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			)
								_UpperCAmelCase      =			AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			-1

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

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

								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			)
								_UpperCAmelCase      =			AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			-1

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

								with CaptureStdout() as cs:
														_UpperCAmelCase      =			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
								_UpperCAmelCase      =			cs.out[:-1]

								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		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
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""distilgpt2"""			)
								_UpperCAmelCase      =			AutoModelForCausalLM.from_pretrained("""distilgpt2"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			-1

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			)
								_UpperCAmelCase      =			AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			-1

								_UpperCAmelCase      =			ids_tensor((1, 5)						,		vocab_size=model.config.vocab_size			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			TextIteratorStreamer(_SCREAMING_SNAKE_CASE						,		timeout=0.001			)
								_UpperCAmelCase      =			{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
								_UpperCAmelCase      =			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			):
														_UpperCAmelCase      =			""""""
														for new_text in streamer:
																				streamer_text += new_text





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[int]:
						if len(snake_case       ) == 0:
												return array

						_UpperCAmelCase    ,					_UpperCAmelCase      =			min(snake_case       ), max(snake_case       )

						# Compute the variables
						_UpperCAmelCase      =			_max - _min + 1
						_UpperCAmelCase    ,					_UpperCAmelCase      =			[0] * holes_range, [0] * holes_range

						# Make the sorting.
						for i in array:
												_UpperCAmelCase      =			i - _min
												_UpperCAmelCase      =			i
												holes_repeat[index] += 1

						# Makes the array back by replacing the numbers.
						_UpperCAmelCase      =			0
						for i in range(snake_case       ):
												while holes_repeat[i] > 0:
																		_UpperCAmelCase      =			holes[i]
																		index += 1
																		holes_repeat[i] -= 1

    # Returns the sorted array.
						return array


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			input("Enter numbers separated by comma:\n")
				a													=			[int(x) for x in user_input.split(",")]
				print(pigeon_sort(unsorted))