

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 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
						__a        =AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__snake_case					)
						__a        =-1

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

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

						self.assertEqual(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
						__a        =AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__snake_case					)
						__a        =-1

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

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

						self.assertEqual(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
						__a        =AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__snake_case					)
						__a        =-1

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

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

						self.assertEqual(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						# 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        =AutoTokenizer.from_pretrained('distilgpt2'					)
						__a        =AutoModelForCausalLM.from_pretrained('distilgpt2'					).to(__snake_case					)
						__a        =-1

						__a        =torch.ones((1, 5)					,  device=__snake_case					).long() * model.config.bos_token_id
						with CaptureStdout() as cs:
									__a        =TextStreamer(__snake_case					,  skip_special_tokens=__snake_case					)
									model.generate(__snake_case					,  max_new_tokens=1					,  do_sample=__snake_case					,  streamer=__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
						__a        =cs.out[:-1]  # Remove the final "\n"
						__a        =tokenizer(__snake_case					,  return_tensors='pt'					)
						self.assertEqual(streamer_text_tokenized.input_ids.shape					,  (1, 1)					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
						__a        =AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__snake_case					)
						__a        =-1

						__a        =ids_tensor((1, 5)					,  vocab_size=model.config.vocab_size					).to(__snake_case					)
						__a        =TextIteratorStreamer(__snake_case					,  timeout=0.001					)
						__a        ={'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
						__a        =Thread(target=model.generate					,  kwargs=__snake_case					)
						thread.start()

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









_lowerCAmelCase					:			Optional[int]  				=			"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_lowerCAmelCase					:			Tuple  				=			[{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase					:			Optional[int]  				=			{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






