

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 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def 				lowercase_		(							self						:		Any )		->							str:
       SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
       SCREAMING_SNAKE_CASE__   =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   -1

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

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

       self.assertEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
       SCREAMING_SNAKE_CASE__   =   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   -1

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

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

       self.assertEqual(__lowerCamelCase     ,       __lowerCamelCase )






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

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

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

       self.assertEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[Any] )		->							List[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
       SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained('''distilgpt2''' )
       SCREAMING_SNAKE_CASE__   =   AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   -1

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







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

       SCREAMING_SNAKE_CASE__   =   ids_tensor((1, 5)     ,       vocab_size=model.config.vocab_size ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   TextIteratorStreamer(__lowerCamelCase     ,       timeout=0.001 )
       SCREAMING_SNAKE_CASE__   =   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
       SCREAMING_SNAKE_CASE__   =   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 ):
            SCREAMING_SNAKE_CASE__   =   ''''''
            for new_text in streamer:
                 streamer_text += new_text





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "vivit"






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		Optional[int]=32     ,       __lowerCamelCase						:		Tuple=[2, 16, 16]     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Optional[Any]=12     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		Any="gelu_fast"     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Any=1e-06     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		Any     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   tubelet_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   qkv_bias

       super().__init__(**__lowerCamelCase )


