



'''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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			-1

													SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor((1, 5)				,    vocab_size=model.config.vocab_size				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			model.generate(lowerCamelCase__				,    max_new_tokens=10				,    do_sample=lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.decode(greedy_ids[0]				)

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

													self.assertEqual(lowerCamelCase__				,    lowerCamelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			-1

													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor((1, 5)				,    vocab_size=model.config.vocab_size				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model.generate(lowerCamelCase__				,    max_new_tokens=10				,    do_sample=lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			tokenizer.decode(greedy_ids[0]				)

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

													self.assertEqual(lowerCamelCase__				,    lowerCamelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
													SCREAMING_SNAKE_CASE					:      str     					=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			-1

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

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

													self.assertEqual(lowerCamelCase__				,    lowerCamelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoTokenizer.from_pretrained('''distilgpt2'''				)
													SCREAMING_SNAKE_CASE					:      Any     					=			AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			-1

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			-1

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ids_tensor((1, 5)				,    vocab_size=model.config.vocab_size				).to(lowerCamelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TextIteratorStreamer(lowerCamelCase__				,    timeout=0.001				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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__				):
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			''''''
																				for new_text in streamer:
																											streamer_text += new_text






'''simple docstring'''



from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id'''					,		['''canonical_dataset_name''', '''org-name/dataset-name''']				)
@pytest.mark.parametrize('''path'''					,		['''filename.csv''', '''filename with blanks.csv''']				)
@pytest.mark.parametrize('''revision'''					,		[None, '''v2''']				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      int     					=			hf_hub_url(repo_id=A					,		path=A					,		revision=A				)
							assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(A				)}"""




