


"""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 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Dict	)->    List[Any]:
           lowerCamelCase					:					Tuple        =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	)
           lowerCamelCase					:					Union[str, Any]        =	AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	).to(__a	)
           lowerCamelCase					:					str        =	-1

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

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

           self.assertEqual(__a     ,		__a	)

     def 		a__						(	self:						str	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	)
           lowerCamelCase					:					Any        =	AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	).to(__a	)
           lowerCamelCase					:					str        =	-1

           lowerCamelCase					:					Optional[Any]        =	ids_tensor((1, 5)     ,		vocab_size=model.config.vocab_size	).to(__a	)
           lowerCamelCase					:					Any        =	model.generate(__a     ,		max_new_tokens=10     ,		do_sample=__a	)
           lowerCamelCase					:					List[Any]        =	tokenizer.decode(greedy_ids[0]	)

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

           self.assertEqual(__a     ,		__a	)

     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[int]        =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	)
           lowerCamelCase					:					Optional[Any]        =	AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	).to(__a	)
           lowerCamelCase					:					Optional[Any]        =	-1

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

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

           self.assertEqual(__a     ,		__a	)

     def 		a__						(	self:						List[str]	)->    str:
           # 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					:					Union[str, Any]        =	AutoTokenizer.from_pretrained("""distilgpt2"""	)
           lowerCamelCase					:					Dict        =	AutoModelForCausalLM.from_pretrained("""distilgpt2"""	).to(__a	)
           lowerCamelCase					:					Optional[int]        =	-1

           lowerCamelCase					:					List[Any]        =	torch.ones((1, 5)     ,		device=__a	).long() * model.config.bos_token_id
           with CaptureStdout() as cs:
                 lowerCamelCase					:					Dict        =	TextStreamer(__a     ,		skip_special_tokens=__a	)
                 model.generate(__a     ,		max_new_tokens=1     ,		do_sample=__a     ,		streamer=__a	)

           # 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(__a     ,		return_tensors="""pt"""	)
           self.assertEqual(streamer_text_tokenized.input_ids.shape     ,		(1, 1)	)







     def 		a__						(	self:						int	)->    List[str]:
           lowerCamelCase					:					Optional[int]        =	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	)
           lowerCamelCase					:					List[Any]        =	AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""	).to(__a	)
           lowerCamelCase					:					Optional[Any]        =	-1

           lowerCamelCase					:					str        =	ids_tensor((1, 5)     ,		vocab_size=model.config.vocab_size	).to(__a	)
           lowerCamelCase					:					Optional[int]        =	TextIteratorStreamer(__a     ,		timeout=0.0_01	)
           lowerCamelCase					:					int        =	{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
           lowerCamelCase					:					Tuple        =	Thread(target=model.generate     ,		kwargs=__a	)
           thread.start()

           # The streamer will timeout after 0.001 seconds, so an exception will be raised
           with self.assertRaises(__a	):
                 lowerCamelCase					:					Optional[int]        =	""""""
                 for new_text in streamer:
                       streamer_text += new_text








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




