






"""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      _lowercase (     unittest.TestCase						):


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







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

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

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

            self.assertEqual(a							,   a						)


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







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

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

            __snake_case      :    List[Any]  			=TextIteratorStreamer(a						)
            __snake_case      :    Union[str, Any]  			={'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
            __snake_case      :    Union[str, Any]  			=Thread(target=model.generate							,   kwargs=a						)
            thread.start()
            __snake_case      :    str  			=''''''
            for new_text in streamer:
                  streamer_text += new_text

            self.assertEqual(a							,   a						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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

            __snake_case      :    str  			=ids_tensor((1, 5)							,   vocab_size=model.config.vocab_size						).to(a						)
            __snake_case      :    Optional[Any]  			=model.generate(a							,   max_new_tokens=1_0							,   do_sample=a						)
            __snake_case      :    Union[str, Any]  			=greedy_ids[:, input_ids.shape[1] :]
            __snake_case      :    str  			=tokenizer.decode(new_greedy_ids[0]						)

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

            self.assertEqual(a							,   a						)


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





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=AutoTokenizer.from_pretrained('''distilgpt2'''						)
            __snake_case      :    Dict  			=AutoModelForCausalLM.from_pretrained('''distilgpt2'''						).to(a						)
            __snake_case      :    Any  			=-1

            __snake_case      :    Optional[int]  			=torch.ones((1, 5)							,   device=a						).long() * model.config.bos_token_id
            with CaptureStdout() as cs:
                  __snake_case      :    Dict  			=TextStreamer(a							,   skip_special_tokens=a						)
                  model.generate(a							,   max_new_tokens=1							,   do_sample=a							,   streamer=a						)

            # 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      :    List[str]  			=cs.out[:-1]  # Remove the final "\n"
            __snake_case      :    List[Any]  			=tokenizer(a							,   return_tensors='''pt'''						)
            self.assertEqual(streamer_text_tokenized.input_ids.shape							,   (1, 1)						)







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





            """simple docstring"""







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

            __snake_case      :    Optional[Any]  			=ids_tensor((1, 5)							,   vocab_size=model.config.vocab_size						).to(a						)
            __snake_case      :    str  			=TextIteratorStreamer(a							,   timeout=0.0_0_1						)
            __snake_case      :    List[Any]  			={'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
            __snake_case      :    List[str]  			=Thread(target=model.generate							,   kwargs=a						)
            thread.start()

            # The streamer will timeout after 0.001 seconds, so an exception will be raised
            with self.assertRaises(a						):
                  __snake_case      :    Any  			=''''''
                  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


UpperCamelCase_	:				Dict										= logging.get_logger(__name__)

UpperCamelCase_	:				Dict										= {
    """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      _lowercase (     lowerCAmelCase						):
      _a   :     Union[str, Any]								=   '''xmod'''


      def __init__(    self				:	Optional[int]							,   a				:	Dict=3_0_5_2_2							,   a				:	Any=7_6_8							,   a				:	str=1_2							,   a				:	Optional[int]=1_2							,   a				:	Any=3_0_7_2							,   a				:	int="gelu"							,   a				:	List[Any]=0.1							,   a				:	Union[str, Any]=0.1							,   a				:	Tuple=5_1_2							,   a				:	str=2							,   a				:	Any=0.0_2							,   a				:	Tuple=1e-12							,   a				:	Optional[int]=1							,   a				:	Any=0							,   a				:	str=2							,   a				:	str="absolute"							,   a				:	List[Any]=True							,   a				:	Optional[Any]=None							,   a				:	Tuple=False							,   a				:	Union[str, Any]=2							,   a				:	Any=False							,   a				:	Dict=True							,   a				:	int=True							,   a				:	str=("en_XX",)							,   a				:	Dict=None							,   **a				:	Tuple							,   ):





            """simple docstring"""







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

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



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







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


