
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

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







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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

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

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

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

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

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

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

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

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

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # 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
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

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

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





