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       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         UpperCAmelCase__								=  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  -1

         UpperCAmelCase__								=  ids_tensor((1, 5)	,  vocab_size=model.config.vocab_size		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  model.generate(__UpperCAmelCase	,  max_new_tokens=1_0	,  do_sample=__UpperCAmelCase		)
         UpperCAmelCase__								=  tokenizer.decode(greedy_ids[0]		)

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

         self.assertEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         UpperCAmelCase__								=  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  -1

         UpperCAmelCase__								=  ids_tensor((1, 5)	,  vocab_size=model.config.vocab_size		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  model.generate(__UpperCAmelCase	,  max_new_tokens=1_0	,  do_sample=__UpperCAmelCase		)
         UpperCAmelCase__								=  tokenizer.decode(greedy_ids[0]		)

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

         self.assertEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         UpperCAmelCase__								=  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  -1

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

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

         self.assertEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						str		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  AutoTokenizer.from_pretrained("distilgpt2"		)
         UpperCAmelCase__								=  AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  -1

         UpperCAmelCase__								=  torch.ones((1, 5)	,  device=__UpperCAmelCase		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
               UpperCAmelCase__								=  TextStreamer(__UpperCAmelCase	,  skip_special_tokens=__UpperCAmelCase		)
               model.generate(__UpperCAmelCase	,  max_new_tokens=1	,  do_sample=__UpperCAmelCase	,  streamer=__UpperCAmelCase		)

         # 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(__UpperCAmelCase	,  return_tensors="pt"		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape	,  (1, 1)		)





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



         UpperCAmelCase__								=  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         UpperCAmelCase__								=  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  -1

         UpperCAmelCase__								=  ids_tensor((1, 5)	,  vocab_size=model.config.vocab_size		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  TextIteratorStreamer(__UpperCAmelCase	,  timeout=0.001		)
         UpperCAmelCase__								=  {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
         UpperCAmelCase__								=  Thread(target=model.generate	,  kwargs=__UpperCAmelCase		)
         thread.start()

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




from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    							= {'tokenization_bertweet': ['BertweetTokenizer']}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

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



