





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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



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

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

         with CaptureStdout() as cs:
            __snake_case								=       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
         __snake_case								=       cs.out[:-1]

         self.assertEqual(a__    ,      a__				)


      def        a			(self	:				int				):





         """simple docstring"""



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

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

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

         self.assertEqual(a__    ,      a__				)


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



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

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

         with CaptureStdout() as cs:
            __snake_case								=       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
         __snake_case								=       cs.out[:-1]

         self.assertEqual(a__    ,      a__				)


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       AutoTokenizer.from_pretrained('''distilgpt2'''				)
         __snake_case								=       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(a__				)
         __snake_case								=       -1

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


      def        a			(self	:				Any				):





         """simple docstring"""



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

         __snake_case								=       ids_tensor((1, 5)    ,      vocab_size=model.config.vocab_size				).to(a__				)
         __snake_case								=       TextIteratorStreamer(a__    ,      timeout=0.0_0_1				)
         __snake_case								=       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
         __snake_case								=       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__				):
            __snake_case								=       ''''''
            for new_text in streamer:
               streamer_text += new_text








from typing import TYPE_CHECKING

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


snake_case_           =      {}

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

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizerFast']


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

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

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

