



"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # For consistency across different places the DisjunctiveConstraint is called,
     # dc.token_ids is a list of integers. It is also initialized only by integers.

     _snake_case   	=      [[1, 2, 4], [1, 2, 3, 4]]
     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)
     self.assertTrue(isinstance(dc.token_ids							,       __lowerCamelCase				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint([torch.LongTensor([1, 2, 4]				), torch.LongTensor([1, 2, 3, 4, 5]				)]				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # We can't have constraints that are complete subsets of another. This leads to a preverse
     # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
     # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
     # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
     # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
     _snake_case   	=      [[1, 2], [1, 2, 3, 4]]

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(__lowerCamelCase				)  # fails here



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(3				)
     _snake_case   	=      stepped is True and completed is True and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 3]				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(4				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2, 4]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 4, 5]				)

     dc.reset()

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 3				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 2				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.remaining() == 0				)
     self.assertTrue(dc.current_seq == [1, 2, 5]				)









'''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 ) )



