






'''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							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      List[str]     							= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
      __snake_case				:      Dict     							= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(lowercase__)
      __snake_case				:      Optional[Any]     							= -1

      __snake_case				:      int     							= ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size).to(lowercase__)
      __snake_case				:      Union[str, Any]     							= model.generate(lowercase__					,   max_new_tokens=1_0					,   do_sample=lowercase__)
      __snake_case				:      Dict     							= tokenizer.decode(greedy_ids[0])

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

      self.assertEqual(lowercase__					,   lowercase__)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
      __snake_case				:      Optional[int]     							= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(lowercase__)
      __snake_case				:      List[Any]     							= -1

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

      __snake_case				:      Dict     							= TextIteratorStreamer(lowercase__)
      __snake_case				:      Dict     							= {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      __snake_case				:      int     							= Thread(target=model.generate					,   kwargs=lowercase__)
      thread.start()
      __snake_case				:      Tuple     							= ""
      for new_text in streamer:
         streamer_text += new_text

      self.assertEqual(lowercase__					,   lowercase__)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      Any     							= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
      __snake_case				:      Union[str, Any]     							= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(lowercase__)
      __snake_case				:      Union[str, Any]     							= -1

      __snake_case				:      Tuple     							= ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size).to(lowercase__)
      __snake_case				:      int     							= model.generate(lowercase__					,   max_new_tokens=1_0					,   do_sample=lowercase__)
      __snake_case				:      Tuple     							= greedy_ids[:, input_ids.shape[1] :]
      __snake_case				:      Tuple     							= tokenizer.decode(new_greedy_ids[0])

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

      self.assertEqual(lowercase__					,   lowercase__)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      str     							= AutoTokenizer.from_pretrained('distilgpt2')
      __snake_case				:      List[str]     							= AutoModelForCausalLM.from_pretrained('distilgpt2').to(lowercase__)
      __snake_case				:      List[str]     							= -1

      __snake_case				:      Tuple     							= torch.ones((1, 5)					,   device=lowercase__).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
         __snake_case				:      List[str]     							= TextStreamer(lowercase__					,   skip_special_tokens=lowercase__)
         model.generate(lowercase__					,   max_new_tokens=1					,   do_sample=lowercase__					,   streamer=lowercase__)

      # 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				:      Optional[int]     							= cs.out[:-1]  # Remove the final "\n"
      __snake_case				:      Dict     							= tokenizer(lowercase__					,   return_tensors='pt')
      self.assertEqual(streamer_text_tokenized.input_ids.shape					,   (1, 1))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      Any     							= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
      __snake_case				:      Tuple     							= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(lowercase__)
      __snake_case				:      str     							= -1

      __snake_case				:      Optional[int]     							= ids_tensor((1, 5)					,   vocab_size=model.config.vocab_size).to(lowercase__)
      __snake_case				:      Tuple     							= TextIteratorStreamer(lowercase__					,   timeout=0.001)
      __snake_case				:      Dict     							= {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      __snake_case				:      Optional[Any]     							= Thread(target=model.generate					,   kwargs=lowercase__)
      thread.start()

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







'''simple docstring'''






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


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias