


'''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 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Tuple ):
    UpperCAmelCase__	:Union[str, Any]      =      3
    UpperCAmelCase__	:Optional[Any]      =      2_5_0

    UpperCAmelCase__	:Dict      =      ids_tensor((batch_size, length)			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      torch.ones((batch_size, length)			,       device=__lowerCamelCase			,       dtype=torch.float ) / length
    return input_ids, scores


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self._get_tensors(5 )

    UpperCAmelCase__	:Optional[int]      =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=1_0 ),
            MaxTimeCriteria(max_time=0.1 ),
        ] )

    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

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

    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[int]      =      self._get_tensors(1_0 )
    self.assertTrue(criteria(__lowerCamelCase			,       __lowerCamelCase ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Union[str, Any]      =      MaxLengthCriteria(max_length=1_0 )

    UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      self._get_tensors(5 )
    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self._get_tensors(9 )
    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      self._get_tensors(1_0 )
    self.assertTrue(criteria(__lowerCamelCase			,       __lowerCamelCase ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      MaxNewTokensCriteria(start_length=5			,       max_new_tokens=5 )

    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      self._get_tensors(5 )
    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      self._get_tensors(9 )
    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[int]      =      self._get_tensors(1_0 )
    self.assertTrue(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__	:List[Any]      =      StoppingCriteriaList([criteria] )
    self.assertEqual(criteria_list.max_length			,       1_0 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      self._get_tensors(5 )

    UpperCAmelCase__	:Dict      =      MaxTimeCriteria(max_time=0.1 )
    self.assertFalse(criteria(__lowerCamelCase			,       __lowerCamelCase ) )

    UpperCAmelCase__	:Optional[int]      =      MaxTimeCriteria(max_time=0.1			,       initial_timestamp=time.time() - 0.2 )
    self.assertTrue(criteria(__lowerCamelCase			,       __lowerCamelCase ) )





 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] )			,       1_0 )

    with self.assertWarns(__lowerCamelCase ):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] )			,       1_1 )

    UpperCAmelCase__	:Tuple      =      validate_stopping_criteria(StoppingCriteriaList()			,       1_1 )

    self.assertEqual(len(__lowerCamelCase )			,       1 )







'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



