





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowercase    : Optional[int]    	=  {
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}

class  __A(						__a ):
     __A	  =       "albert"

     def __init__(      self,       A=3_0000,       A=128,       A=4096,       A=12,       A=1,       A=64,       A=1_6384,       A=1,       A="gelu_new",       A=0,       A=0,       A=512,       A=2,       A=0.02,       A=1E-1_2,       A=0.1,       A="absolute",       A=0,       A=2,       A=3,       **A,       ):





            """simple docstring"""

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

            _UpperCamelCase              =						vocab_size
            _UpperCamelCase              =						embedding_size
            _UpperCamelCase              =						hidden_size
            _UpperCamelCase              =						num_hidden_layers
            _UpperCamelCase              =						num_hidden_groups
            _UpperCamelCase              =						num_attention_heads
            _UpperCamelCase              =						inner_group_num
            _UpperCamelCase              =						hidden_act
            _UpperCamelCase              =						intermediate_size
            _UpperCamelCase              =						hidden_dropout_prob
            _UpperCamelCase              =						attention_probs_dropout_prob
            _UpperCamelCase              =						max_position_embeddings
            _UpperCamelCase              =						type_vocab_size
            _UpperCamelCase              =						initializer_range
            _UpperCamelCase              =						layer_norm_eps
            _UpperCamelCase              =						classifier_dropout_prob
            _UpperCamelCase              =						position_embedding_type






class  __A(						__a ):

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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













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  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

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

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






