


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):




     '''simple docstring'''


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

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

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Dict				    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
            _A      :       Any				    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__lowerCamelCase)
            _A      :       Optional[int]				    = -1

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

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


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

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

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Any:
            # 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      :       List[str]				    = AutoTokenizer.from_pretrained("distilgpt2")
            _A      :       Any				    = AutoModelForCausalLM.from_pretrained("distilgpt2").to(__lowerCamelCase)
            _A      :       Any				    = -1

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


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

            _A      :       Any				    = ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size).to(__lowerCamelCase)
            _A      :       Any				    = TextIteratorStreamer(__lowerCamelCase ,						timeout=0.0_0_1)
            _A      :       List[Any]				    = {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
            _A      :       str				    = 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):
                   _A      :       Optional[Any]				    = ""
                   for new_text in streamer:
                          streamer_text += new_text




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__      		= {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SpeechT5ForSpeechToText',
        'SpeechT5ForSpeechToSpeech',
        'SpeechT5ForTextToSpeech',
        'SpeechT5Model',
        'SpeechT5PreTrainedModel',
        'SpeechT5HifiGan',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
