





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import BertGenerationDecoder, BertGenerationEncoder




class 			__UpperCamelCase    :





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase=13				,							_UpperCamelCase=7				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=99				,							_UpperCamelCase=32				,							_UpperCamelCase=5				,							_UpperCamelCase=4				,							_UpperCamelCase=37				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=50				,							_UpperCamelCase=0.02				,							_UpperCamelCase=True				,							_UpperCamelCase=None				,							):
        _UpperCAmelCase           =   parent
        _UpperCAmelCase           =   batch_size
        _UpperCAmelCase           =   seq_length
        _UpperCAmelCase           =   is_training
        _UpperCAmelCase           =   use_input_mask
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   use_labels
        _UpperCAmelCase           =   scope





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size					)

        _UpperCAmelCase           =   None
        if self.use_input_mask:
           _UpperCAmelCase           =   random_attention_mask([self.batch_size, self.seq_length]					)

        if self.use_labels:
           _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size					)

        _UpperCAmelCase           =   self.get_config()

        return config, input_ids, input_mask, token_labels





     def 		UpperCamelCase(     self					):
        return BertGenerationConfig(
            vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							is_decoder=_UpperCamelCase				,							initializer_range=self.initializer_range				,							)





     def 		UpperCamelCase(     self					):
        (
            (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      
        )           =   self.prepare_config_and_inputs()

        _UpperCAmelCase           =   True
        _UpperCAmelCase           =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
        _UpperCAmelCase           =   ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2					)

        return (
            config,
            input_ids,
            input_mask,
            token_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   BertGenerationEncoder(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase					)
        _UpperCAmelCase           =   model(_UpperCamelCase					)
        self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   True
        _UpperCAmelCase           =   BertGenerationEncoder(config=_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							encoder_hidden_states=_UpperCamelCase				,							encoder_attention_mask=_UpperCamelCase				,							)
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							encoder_hidden_states=_UpperCamelCase				,							)
        self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   True
        _UpperCAmelCase           =   True
        _UpperCAmelCase           =   BertGenerationDecoder(config=_UpperCamelCase					).to(_UpperCamelCase					).eval()

        # first forward pass
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							encoder_hidden_states=_UpperCamelCase				,							encoder_attention_mask=_UpperCamelCase				,							use_cache=_UpperCamelCase				,							)
        _UpperCAmelCase           =   outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _UpperCAmelCase           =   ids_tensor((self.batch_size, 3)				,							config.vocab_size					)
        _UpperCAmelCase           =   ids_tensor((self.batch_size, 3)				,							vocab_size=2					)

        # append to next input_ids and
        _UpperCAmelCase           =   torch.cat([input_ids, next_tokens]				,							dim=-1					)
        _UpperCAmelCase           =   torch.cat([input_mask, next_mask]				,							dim=-1					)

        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							encoder_hidden_states=_UpperCamelCase				,							encoder_attention_mask=_UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							)['''hidden_states'''][0]
        _UpperCAmelCase           =   model(
            _UpperCamelCase				,							attention_mask=_UpperCamelCase				,							encoder_hidden_states=_UpperCamelCase				,							encoder_attention_mask=_UpperCamelCase				,							past_key_values=_UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							)['''hidden_states'''][0]

        # select random slice
        _UpperCAmelCase           =   ids_tensor((1,)				,							output_from_past.shape[-1]					).item()
        _UpperCAmelCase           =   output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase           =   output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_UpperCamelCase				,							_UpperCamelCase				,							atol=1e-3					)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							*_UpperCamelCase				,							):
        _UpperCAmelCase           =   BertGenerationDecoder(_UpperCamelCase					)
        model.to(_UpperCamelCase					)
        model.eval()
        _UpperCAmelCase           =   model(_UpperCamelCase				,							attention_mask=_UpperCamelCase				,							labels=_UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   self.prepare_config_and_inputs()
        _UpperCAmelCase           =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict




@require_torch
class 			__UpperCamelCase    (							A__	, A__	, A__	, unittest.TestCase    ):
     __A	:  Dict							  =   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     __A	:  Tuple							  =   (BertGenerationDecoder,) if is_torch_available() else ()
     __A	:  Tuple							  =   (
         {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
         if is_torch_available()
         else {}
     )





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   BertGenerationEncoderTester(self					)
        _UpperCAmelCase           =   ConfigTester(self				,							config_class=_UpperCamelCase				,							hidden_size=37					)





     def 		UpperCamelCase(     self					):
        self.config_tester.run_common_tests()





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase           =   '''bert'''
        self.model_tester.create_and_check_model(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        # This regression test was failing with PyTorch < 1.3
        (
            (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      (
            _UpperCAmelCase
        )       ,      
        )           =   self.model_tester.prepare_config_and_inputs_for_decoder()

        _UpperCAmelCase           =   None

        self.model_tester.create_and_check_model_as_decoder(
            _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_for_causal_lm(*_UpperCamelCase					)





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
        self.assertIsNotNone(_UpperCamelCase					)




@require_torch
class 			__UpperCamelCase    (							unittest.TestCase    ):





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
        _UpperCAmelCase           =   torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]					)
        with torch.no_grad():
           _UpperCAmelCase           =   model(_UpperCamelCase					)[0]
        _UpperCAmelCase           =   torch.Size([1, 8, 1024]					)
        self.assertEqual(output.shape				,							_UpperCamelCase					)
        _UpperCAmelCase           =   torch.tensor(
            [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]					)
        self.assertTrue(torch.allclose(output[:, :3, :3]				,							_UpperCamelCase				,							atol=1e-4					)					)




@require_torch
class 			__UpperCamelCase    (							unittest.TestCase    ):





     @slow
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
        _UpperCAmelCase           =   torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]					)
        with torch.no_grad():
           _UpperCAmelCase           =   model(_UpperCamelCase					)[0]
        _UpperCAmelCase           =   torch.Size([1, 8, 50358]					)
        self.assertEqual(output.shape				,							_UpperCamelCase					)
        _UpperCAmelCase           =   torch.tensor(
            [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]					)
        self.assertTrue(torch.allclose(output[:, :3, :3]				,							_UpperCamelCase				,							atol=1e-4					)					)






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


lowercase__       : List[str]         =       logging.get_logger(__name__)

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              _UpperCAmelCase		,      tokenizer_file=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					List[str]						=			[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                   'tokenizer.')

          if not os.path.isdir(_UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
          __A		:					List[Any]						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCAmelCase):
               copyfile(self.vocab_file		,      _UpperCAmelCase)

          return (out_vocab_file,)