


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      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Optional[Any]				    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
            _A      :       Optional[int]				    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__lowerCamelCase)
            _A      :       str				    = -1

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

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


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

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

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Tuple				    = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
            _A      :       Dict				    = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(__lowerCamelCase)
            _A      :       Union[str, Any]				    = -1

            _A      :       Dict				    = ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size).to(__lowerCamelCase)
            _A      :       Optional[int]				    = model.generate(__lowerCamelCase ,						max_new_tokens=1_0 ,						do_sample=__lowerCamelCase)
            _A      :       List[str]				    = greedy_ids[:, input_ids.shape[1] :]
            _A      :       Dict				    = tokenizer.decode(new_greedy_ids[0])

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

            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Any:
            # 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
            _A      :       List[str]				    = AutoTokenizer.from_pretrained("distilgpt2")
            _A      :       Any				    = AutoModelForCausalLM.from_pretrained("distilgpt2").to(__lowerCamelCase)
            _A      :       Any				    = -1

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


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

            _A      :       Any				    = ids_tensor((1, 5) ,						vocab_size=model.config.vocab_size).to(__lowerCamelCase)
            _A      :       Any				    = TextIteratorStreamer(__lowerCamelCase ,						timeout=0.0_0_1)
            _A      :       List[Any]				    = {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
            _A      :       str				    = 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):
                   _A      :       Optional[Any]				    = ""
                   for new_text in streamer:
                          streamer_text += new_text

from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






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

							'''simple docstring'''


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