




'''simple docstring'''

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   A_ (		self    :					str       ,				lowercase_    :					List[str]					):
									snake_case_									=				3
									snake_case_									=				250

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


				def   A_ (		self    :					List[Any]					):
									snake_case_			,snake_case_									=				self._get_tensors(5					)

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

									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(9					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(10					)
									self.assertTrue(criteria(lowercase_       ,				lowercase_					)					)


				def   A_ (		self    :					str					):
									snake_case_									=				MaxLengthCriteria(max_length=10					)

									snake_case_			,snake_case_									=				self._get_tensors(5					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(9					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(10					)
									self.assertTrue(criteria(lowercase_       ,				lowercase_					)					)


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				MaxNewTokensCriteria(start_length=5       ,				max_new_tokens=5					)

									snake_case_			,snake_case_									=				self._get_tensors(5					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(9					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_			,snake_case_									=				self._get_tensors(10					)
									self.assertTrue(criteria(lowercase_       ,				lowercase_					)					)

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


				def   A_ (		self    :					List[Any]					):
									snake_case_			,snake_case_									=				self._get_tensors(5					)

									snake_case_									=				MaxTimeCriteria(max_time=0.1					)
									self.assertFalse(criteria(lowercase_       ,				lowercase_					)					)

									snake_case_									=				MaxTimeCriteria(max_time=0.1       ,				initial_timestamp=time.time() - 0.2					)
									self.assertTrue(criteria(lowercase_       ,				lowercase_					)					)


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

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

									snake_case_									=				validate_stopping_criteria(StoppingCriteriaList()       ,				11					)

									self.assertEqual(len(lowercase_					)       ,				1					)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

