






"""simple docstring"""






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.albert.modeling_flax_albert import (
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForPreTraining,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertModel,
     )





class       snake_case_       (    unittest.TestCase       ):



 """simple docstring"""

 def __init__(      self  ,					__a  ,					__a=13  ,					__a=7  ,					__a=True  ,					__a=True  ,					__a=True  ,					__a=True  ,					__a=99  ,					__a=32  ,					__a=5  ,					__a=4  ,					__a=37  ,					__a="gelu"  ,					__a=0.1  ,					__a=0.1  ,					__a=512  ,					__a=16  ,					__a=2  ,					__a=0.02  ,					__a=4  ,					):


     """simple docstring"""

     A__							 =       parent
     A__							 =       batch_size
     A__							 =       seq_length
     A__							 =       is_training
     A__							 =       use_attention_mask
     A__							 =       use_token_type_ids
     A__							 =       use_labels
     A__							 =       vocab_size
     A__							 =       hidden_size
     A__							 =       num_hidden_layers
     A__							 =       num_attention_heads
     A__							 =       intermediate_size
     A__							 =       hidden_act
     A__							 =       hidden_dropout_prob
     A__							 =       attention_probs_dropout_prob
     A__							 =       max_position_embeddings
     A__							 =       type_vocab_size
     A__							 =       type_sequence_label_size
     A__							 =       initializer_range
     A__							 =       num_choices


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size						)

     A__							 =       None
     if self.use_attention_mask:
         A__							 =       random_attention_mask([self.batch_size, self.seq_length]						)

     A__							 =       None
     if self.use_token_type_ids:
         A__							 =       ids_tensor([self.batch_size, self.seq_length]  ,					self.type_vocab_size						)

     A__							 =       AlbertConfig(
         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  ,					type_vocab_size=self.type_vocab_size  ,					is_decoder=__a  ,					initializer_range=self.initializer_range  ,					)

     return config, input_ids, token_type_ids, attention_mask






 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.prepare_config_and_inputs()
     A__							 =       config_and_inputs
     A__							 =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
     return config, inputs_dict





@require_flax
class       snake_case_       (    __lowercase    ,							unittest.TestCase       ):



 """simple docstring"""

 SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       FlaxAlbertModelTester(self						)






 @slow
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     for model_class_name in self.all_model_classes:
         A__							 =       model_class_name.from_pretrained('albert-base-v2'						)
         A__							 =       model(np.ones((1, 1)						)						)
         self.assertIsNotNone(__a						)





@require_flax
class       snake_case_       (    unittest.TestCase       ):



 """simple docstring"""

 @slow
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       FlaxAlbertModel.from_pretrained('albert-base-v2'						)
     A__							 =       np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
     A__							 =       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
     A__							 =       model(__a  ,					attention_mask=__a						)[0]
     A__							 =       (1, 11, 768)
     self.assertEqual(output.shape  ,					__a						)
     A__							 =       np.array(
         [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]						)

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]  ,					__a  ,					atol=1E-4						)						)










"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(
					    __a  ,					__a  ,					tokenizer_file=__a  ,					unk_token=__a  ,					bos_token=__a  ,					eos_token=__a  ,					add_prefix_space=__a  ,					**__a  ,					)

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


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


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

					if len(__a						) > self.model_max_length:
									A__							 =       input_ids[-self.model_max_length :]
					return input_ids


