
'''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 		__lowercase (     unittest.TestCase			):

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					List[Any]   =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
								lowerCamelCase_    :					List[Any]   =						AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(_snake_case					)
								lowerCamelCase_    :					Optional[int]   =						-1

								lowerCamelCase_    :					str   =						ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size					).to(_snake_case					)
								lowerCamelCase_    :					str   =						model.generate(_snake_case	,      max_new_tokens=1_0	,      do_sample=_snake_case					)
								lowerCamelCase_    :					Any   =						tokenizer.decode(greedy_ids[0]					)

								with CaptureStdout() as cs:
													lowerCamelCase_    :					Union[str, Any]   =						TextStreamer(_snake_case					)
													model.generate(_snake_case	,      max_new_tokens=1_0	,      do_sample=_snake_case	,      streamer=_snake_case					)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								lowerCamelCase_    :					Union[str, Any]   =						cs.out[:-1]

								self.assertEqual(_snake_case	,      _snake_case					)

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					str   =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
								lowerCamelCase_    :					Dict   =						AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(_snake_case					)
								lowerCamelCase_    :					Union[str, Any]   =						-1

								lowerCamelCase_    :					List[Any]   =						ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size					).to(_snake_case					)
								lowerCamelCase_    :					Dict   =						model.generate(_snake_case	,      max_new_tokens=1_0	,      do_sample=_snake_case					)
								lowerCamelCase_    :					Union[str, Any]   =						tokenizer.decode(greedy_ids[0]					)

								lowerCamelCase_    :					List[Any]   =						TextIteratorStreamer(_snake_case					)
								lowerCamelCase_    :					str   =						{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
								lowerCamelCase_    :					Optional[Any]   =						Thread(target=model.generate	,      kwargs=_snake_case					)
								thread.start()
								lowerCamelCase_    :					Optional[Any]   =						''''''
								for new_text in streamer:
													streamer_text += new_text

								self.assertEqual(_snake_case	,      _snake_case					)

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Dict   =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
								lowerCamelCase_    :					List[str]   =						AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(_snake_case					)
								lowerCamelCase_    :					Optional[Any]   =						-1

								lowerCamelCase_    :					Optional[Any]   =						ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size					).to(_snake_case					)
								lowerCamelCase_    :					List[str]   =						model.generate(_snake_case	,      max_new_tokens=1_0	,      do_sample=_snake_case					)
								lowerCamelCase_    :					Dict   =						greedy_ids[:, input_ids.shape[1] :]
								lowerCamelCase_    :					int   =						tokenizer.decode(new_greedy_ids[0]					)

								with CaptureStdout() as cs:
													lowerCamelCase_    :					int   =						TextStreamer(_snake_case	,      skip_prompt=_snake_case					)
													model.generate(_snake_case	,      max_new_tokens=1_0	,      do_sample=_snake_case	,      streamer=_snake_case					)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								lowerCamelCase_    :					List[Any]   =						cs.out[:-1]

								self.assertEqual(_snake_case	,      _snake_case					)

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Any   =						AutoTokenizer.from_pretrained('''distilgpt2'''					)
								lowerCamelCase_    :					Dict   =						AutoModelForCausalLM.from_pretrained('''distilgpt2'''					).to(_snake_case					)
								lowerCamelCase_    :					Any   =						-1

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






			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Tuple   =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					)
								lowerCamelCase_    :					Optional[int]   =						AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''					).to(_snake_case					)
								lowerCamelCase_    :					Union[str, Any]   =						-1

								lowerCamelCase_    :					List[Any]   =						ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size					).to(_snake_case					)
								lowerCamelCase_    :					Tuple   =						TextIteratorStreamer(_snake_case	,      timeout=0.0_01					)
								lowerCamelCase_    :					Any   =						{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
								lowerCamelCase_    :					int   =						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					):
													lowerCamelCase_    :					Optional[Any]   =						''''''
													for new_text in streamer:
																		streamer_text += new_text








snake_case_ : Dict    		=       {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}






