





"""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 					_SCREAMING_SNAKE_CASE(   unittest.TestCase  ):




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :Dict						=  ids_tensor((1, 5)		,vocab_size=model.config.vocab_size							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model.generate(SCREAMING_SNAKE_CASE__		,max_new_tokens=10		,do_sample=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  tokenizer.decode(greedy_ids[0]							)

             with CaptureStdout() as cs:
                   __SCREAMING_SNAKE_CASE   :str						=  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
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  cs.out[:-1]

             self.assertEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






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

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

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

             self.assertEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  -1

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

             with CaptureStdout() as cs:
                   __SCREAMING_SNAKE_CASE   :Dict						=  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
             __SCREAMING_SNAKE_CASE   :str						=  cs.out[:-1]

             self.assertEqual(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  AutoTokenizer.from_pretrained('''distilgpt2'''							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  AutoModelForCausalLM.from_pretrained('''distilgpt2'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  -1

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




       def     _UpperCamelCase     (						self							)			->      Union[str, Any]:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ids_tensor((1, 5)		,vocab_size=model.config.vocab_size							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  TextIteratorStreamer(SCREAMING_SNAKE_CASE__		,timeout=0.0_0_1							)
             __SCREAMING_SNAKE_CASE   :int						=  {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
             __SCREAMING_SNAKE_CASE   :List[Any]						=  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__							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  ''''''
                   for new_text in streamer:
                         streamer_text += new_text






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)