



import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)




@require_torch
class 							lowerCAmelCase__  (	unittest.TestCase						):







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Any):
											A__	:							str						    = 3
											A__	:							Optional[Any]						    = 250

											A__	:							List[Any]						    = ids_tensor((batch_size, length)			,		_A)
											A__	:							List[Any]						    = torch.ones((batch_size, length)			,		device=_A			,		dtype=torch.float) / length
											return input_ids, scores







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = self._get_tensors(5)

											A__	:							int						    = StoppingCriteriaList(
											    [
											        MaxLengthCriteria(max_length=10),
											        MaxTimeCriteria(max_time=0.1),
											    ])

											self.assertFalse(criteria(_A			,		_A))

											A__	:							Dict						    = self._get_tensors(9)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							List[Any]						    = self._get_tensors(10)
											self.assertTrue(criteria(_A			,		_A))







					def  _lowercase			(       self      :     Any):
											A__	:							List[str]						    = MaxLengthCriteria(max_length=10)

											A__	:							List[str]						    = self._get_tensors(5)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							Dict						    = self._get_tensors(9)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							Any						    = self._get_tensors(10)
											self.assertTrue(criteria(_A			,		_A))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Tuple						    = MaxNewTokensCriteria(start_length=5			,		max_new_tokens=5)

											A__	:							List[Any]						    = self._get_tensors(5)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							Optional[Any]						    = self._get_tensors(9)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							Tuple						    = self._get_tensors(10)
											self.assertTrue(criteria(_A			,		_A))

											A__	:							str						    = StoppingCriteriaList([criteria])
											self.assertEqual(criteria_list.max_length			,		10)







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._get_tensors(5)

											A__	:							Tuple						    = MaxTimeCriteria(max_time=0.1)
											self.assertFalse(criteria(_A			,		_A))

											A__	:							Dict						    = MaxTimeCriteria(max_time=0.1			,		initial_timestamp=time.time() - 0.2)
											self.assertTrue(criteria(_A			,		_A))







					def  _lowercase			(       self      :     Dict):
											validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])			,		10)

											with self.assertWarns(_A):
																	validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])			,		11)

											A__	:							Optional[int]						    = validate_stopping_criteria(StoppingCriteriaList()			,		11)

											self.assertEqual(len(_A)			,		1)





from collections import OrderedDict
from typing import Mapping

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


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

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])