




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 			_UpperCamelCase      (				unittest.TestCase		):
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :int      )     ->			List[str]:
       UpperCAmelCase__							 =						3
       UpperCAmelCase__							 =						250

       UpperCAmelCase__							 =						ids_tensor((batch_size, length) ,      lowerCamelCase      )
       UpperCAmelCase__							 =						torch.ones((batch_size, length) ,      device=lowerCamelCase ,      dtype=torch.float      ) / length
       return input_ids, scores
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(5      )

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

       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(9      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(10      )
       self.assertTrue(criteria(lowerCamelCase ,      lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			str:
       UpperCAmelCase__							 =						MaxLengthCriteria(max_length=10      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(5      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(9      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(10      )
       self.assertTrue(criteria(lowerCamelCase ,      lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[str]:
       UpperCAmelCase__							 =						MaxNewTokensCriteria(start_length=5 ,      max_new_tokens=5      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(5      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(9      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(10      )
       self.assertTrue(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						StoppingCriteriaList([criteria]      )
       self.assertEqual(criteria_list.max_length ,      10      )
      def 	UpperCAmelCase_						(			self   :Any      )     ->			int:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self._get_tensors(5      )

       UpperCAmelCase__							 =						MaxTimeCriteria(max_time=0.1      )
       self.assertFalse(criteria(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						MaxTimeCriteria(max_time=0.1 ,      initial_timestamp=time.time() - 0.2      )
       self.assertTrue(criteria(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ) ,      10      )

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

       UpperCAmelCase__							 =						validate_stopping_criteria(StoppingCriteriaList() ,      11      )

       self.assertEqual(len(lowerCamelCase      ) ,      1      )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





