

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       :		Optional[int] 		=    3
										_UpperCAmelCase       :		Optional[Any] 		=    250

										_UpperCAmelCase       :		Any 		=    ids_tensor((batch_size, length)						,					A_	)
										_UpperCAmelCase       :		str 		=    torch.ones((batch_size, length)						,					device=A_						,					dtype=torch.float	) / length
										return input_ids, scores


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    self._get_tensors(5	)

										_UpperCAmelCase       :		Tuple 		=    StoppingCriteriaList(
										    [
										        MaxLengthCriteria(max_length=10	),
										        MaxTimeCriteria(max_time=0.1	),
										    ]	)

										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self._get_tensors(9	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    self._get_tensors(10	)
										self.assertTrue(criteria(A_						,					A_	)	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    MaxLengthCriteria(max_length=10	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    self._get_tensors(5	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    self._get_tensors(9	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    self._get_tensors(10	)
										self.assertTrue(criteria(A_						,					A_	)	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    MaxNewTokensCriteria(start_length=5						,					max_new_tokens=5	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    self._get_tensors(5	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    self._get_tensors(9	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    self._get_tensors(10	)
										self.assertTrue(criteria(A_						,					A_	)	)

										_UpperCAmelCase       :		Tuple 		=    StoppingCriteriaList([criteria]	)
										self.assertEqual(criteria_list.max_length						,					10	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    self._get_tensors(5	)

										_UpperCAmelCase       :		Optional[int] 		=    MaxTimeCriteria(max_time=0.1	)
										self.assertFalse(criteria(A_						,					A_	)	)

										_UpperCAmelCase       :		Tuple 		=    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       :		int 		=    validate_stopping_criteria(StoppingCriteriaList()						,					11	)

										self.assertEqual(len(A_	)						,					1	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


