



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.xglm.modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				)

@require_tf
class       snake_case__:

					"""simple docstring"""






					lowercase_								=		XGLMConfig
					lowercase_								=		{}
					lowercase_								=		"gelu"







					def __init__(							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	List[str]=14      ,   SCREAMING_SNAKE_CASE					:	Dict=7      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=True      ,   SCREAMING_SNAKE_CASE					:	Dict=True      ,   SCREAMING_SNAKE_CASE					:	str=True      ,   SCREAMING_SNAKE_CASE					:	Optional[int]=99      ,   SCREAMING_SNAKE_CASE					:	int=32      ,   SCREAMING_SNAKE_CASE					:	str=2      ,   SCREAMING_SNAKE_CASE					:	Tuple=4      ,   SCREAMING_SNAKE_CASE					:	Dict=37      ,   SCREAMING_SNAKE_CASE					:	str="gelu"      ,   SCREAMING_SNAKE_CASE					:	List[str]=0.1      ,   SCREAMING_SNAKE_CASE					:	Dict=0.1      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=512      ,   SCREAMING_SNAKE_CASE					:	str=0.02      ,   ):
									lowercase__     :    Union[str, Any]													=     parent
									lowercase__     :    List[Any]													=     batch_size
									lowercase__     :    List[str]													=     seq_length
									lowercase__     :    Tuple													=     is_training
									lowercase__     :    int													=     use_input_mask
									lowercase__     :    List[Any]													=     use_labels
									lowercase__     :    Dict													=     vocab_size
									lowercase__     :    int													=     d_model
									lowercase__     :    Union[str, Any]													=     num_hidden_layers
									lowercase__     :    int													=     num_attention_heads
									lowercase__     :    Union[str, Any]													=     ffn_dim
									lowercase__     :    List[str]													=     activation_function
									lowercase__     :    Dict													=     activation_dropout
									lowercase__     :    int													=     attention_dropout
									lowercase__     :    List[Any]													=     max_position_embeddings
									lowercase__     :    str													=     initializer_range
									lowercase__     :    Union[str, Any]													=     None
									lowercase__     :    int													=     0
									lowercase__     :    Dict													=     2
									lowercase__     :    Tuple													=     1







					def  snake_case     (							self					:	Any     ):
									return XGLMConfig.from_pretrained("facebook/xglm-564M"     )







					def  snake_case     (							self					:	str     ):
									lowercase__     :    Optional[Any]													=     tf.clip_by_value(
									    ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size     )      ,   clip_value_min=0      ,   clip_value_max=3     )

									lowercase__     :    Union[str, Any]													=     None
									if self.use_input_mask:
													lowercase__     :    List[str]													=     random_attention_mask([self.batch_size, self.seq_length]     )

									lowercase__     :    Dict													=     self.get_config()

									lowercase__     :    List[Any]													=     floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,   2     )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)







					def  snake_case     (							self					:	Tuple     ):
									return XGLMConfig(
									    vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   num_layers=self.num_hidden_layers      ,   attention_heads=self.num_attention_heads      ,   ffn_dim=self.ffn_dim      ,   activation_function=self.activation_function      ,   activation_dropout=self.activation_dropout      ,   attention_dropout=self.attention_dropout      ,   max_position_embeddings=self.max_position_embeddings      ,   initializer_range=self.initializer_range      ,   use_cache=A_      ,   bos_token_id=self.bos_token_id      ,   eos_token_id=self.eos_token_id      ,   pad_token_id=self.pad_token_id      ,   return_dict=A_      ,   )







					def  snake_case     (							self					:	Dict     ):
									lowercase__     :    List[str]													=     self.prepare_config_and_inputs()

									(
									    (
									    lowercase__      
									)							,							(
									    lowercase__      
									)							,							(
									    lowercase__      
									)							,							(
									    lowercase__      
									)							,							      
									)     :    int													=     config_and_inputs

									lowercase__     :    List[Any]													=     {
									    "input_ids": input_ids,
									    "head_mask": head_mask,
									}

									return config, inputs_dict






@require_tf
class       snake_case__(A_				,			A_				,			unittest.TestCase			):

					"""simple docstring"""






					lowercase_								=		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					lowercase_								=		(TFXGLMForCausalLM,) if is_tf_available() else ()
					lowercase_								=		(
					    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
					)
					lowercase_								=		False
					lowercase_								=		False
					lowercase_								=		False







					def  snake_case     (							self					:	Optional[Any]     ):
									lowercase__     :    List[str]													=     TFXGLMModelTester(self     )
									lowercase__     :    Union[str, Any]													=     ConfigTester(self      ,   config_class=A_      ,   n_embd=37     )







					def  snake_case     (							self					:	Any     ):
									self.config_tester.run_common_tests()







					@slow
					def  snake_case     (							self					:	Union[str, Any]     ):
									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase__     :    List[Any]													=     TFXGLMModel.from_pretrained(A_     )
													self.assertIsNotNone(A_     )







					@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."     )
					def  snake_case     (							self					:	List[str]     ):
									super().test_resize_token_embeddings()






@require_tf
class       snake_case__(unittest.TestCase			):

					"""simple docstring"""






					@slow
					def  snake_case     (							self					:	Any      ,   SCREAMING_SNAKE_CASE					:	Optional[int]=True     ):
									lowercase__     :    Optional[Any]													=     TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
									lowercase__     :    List[str]													=     tf.convert_to_tensor([[2, 268, 9_865]]      ,   dtype=tf.intaa     )  # The dog
									# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
									# fmt: off
									lowercase__     :    Dict													=     [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
									# fmt: on
									lowercase__     :    List[Any]													=     model.generate(A_      ,   do_sample=A_      ,   num_beams=1     )
									if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist()      ,   A_     )







					@slow
					def  snake_case     (							self					:	Optional[Any]     ):
									lowercase__     :    List[str]													=     XGLMTokenizer.from_pretrained("facebook/xglm-564M"     )
									lowercase__     :    List[Any]													=     TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )

									tf.random.set_seed(0     )
									lowercase__     :    Dict													=     tokenizer("Today is a nice day and"      ,   return_tensors="tf"     )
									lowercase__     :    List[str]													=     tokenized.input_ids
									# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
									with tf.device(":/CPU:0"     ):
													lowercase__     :    str													=     model.generate(A_      ,   do_sample=A_      ,   seed=[7, 0]     )
									lowercase__     :    Union[str, Any]													=     tokenizer.decode(output_ids[0]      ,   skip_special_tokens=A_     )

									lowercase__     :    List[str]													=     (
									    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
									)
									self.assertEqual(A_      ,   A_     )







					@slow
					def  snake_case     (							self					:	List[str]     ):
									lowercase__     :    Any													=     TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"     )
									lowercase__     :    str													=     XGLMTokenizer.from_pretrained("facebook/xglm-564M"     )

									lowercase__     :    Union[str, Any]													=     "left"

									# use different length sentences to test batching
									lowercase__     :    int													=     [
									    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
									    "left-padding, such as in batched generation. The output for the sequence below should be the same "
									    "regardless of whether left padding is applied or not. When",
									    "Hello, my dog is a little",
									]

									lowercase__     :    str													=     tokenizer(A_      ,   return_tensors="tf"      ,   padding=A_     )
									lowercase__     :    Optional[Any]													=     inputs["input_ids"]

									lowercase__     :    List[str]													=     model.generate(input_ids=A_      ,   attention_mask=inputs["attention_mask"]      ,   max_new_tokens=12     )

									lowercase__     :    Dict													=     tokenizer(sentences[0]      ,   return_tensors="tf"     ).input_ids
									lowercase__     :    Union[str, Any]													=     model.generate(input_ids=A_      ,   max_new_tokens=12     )

									lowercase__     :    Any													=     tokenizer(sentences[1]      ,   return_tensors="tf"     ).input_ids
									lowercase__     :    List[Any]													=     model.generate(input_ids=A_      ,   max_new_tokens=12     )

									lowercase__     :    Optional[int]													=     tokenizer.batch_decode(A_      ,   skip_special_tokens=A_     )
									lowercase__     :    Tuple													=     tokenizer.decode(output_non_padded[0]      ,   skip_special_tokens=A_     )
									lowercase__     :    Dict													=     tokenizer.decode(output_padded[0]      ,   skip_special_tokens=A_     )

									lowercase__     :    Optional[Any]													=     [
									    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
									    "left-padding, such as in batched generation. The output for the sequence below should be the same "
									    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
									    "a single",
									    "Hello, my dog is a little bit of a shy one, but he is very friendly",
									]
									self.assertListEqual(A_      ,   A_     )
									self.assertListEqual(A_      ,   [non_padded_sentence, padded_sentence]     )








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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						'▁'

_A		       =						{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_A		       =						{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_A		       =						{'vinai/bartpho-syllable': 1024}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		str      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_="<mask>"					,   A_ = None					,   **A_					,   )  ->       None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else mask_token

         __UpperCamelCase    							={} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=A_					,   eos_token=A_					,   unk_token=A_					,   sep_token=A_					,   cls_token=A_					,   pad_token=A_					,   mask_token=A_					,   sp_model_kwargs=self.sp_model_kwargs					,   **A_					,   )

         __UpperCamelCase    							=vocab_file
         __UpperCamelCase    							=monolingual_vocab_file
         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         # Load the reduced vocab

         # Keep order of special tokens for backward compatibility
         __UpperCamelCase    							={}
         __UpperCamelCase    							=0
         for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(A_    ) not in self.fairseq_tokens_to_ids:
                   __UpperCamelCase    							=cnt
                   cnt += 1
         with open(A_					,   'r'					,   encoding='utf-8'    ) as f:
              for line in f.readlines():
                   __UpperCamelCase    							=line.strip().split()[0]
                   __UpperCamelCase    							=len(self.fairseq_tokens_to_ids    )
         if str(A_    ) not in self.fairseq_tokens_to_ids:
              __UpperCamelCase    							=len(self.fairseq_tokens_to_ids    )

         __UpperCamelCase    							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self    )  ->       Any:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=d

         # for backward compatibility
         if not hasattr(self					,   'sp_model_kwargs'    ):
              __UpperCamelCase    							={}

         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __UpperCamelCase    							=[self.cls_token_id]
         __UpperCamelCase    							=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_					,   token_ids_a=A_					,   already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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]






    @property
    def _a					(			self    )  ->       Any:
         return len(self.fairseq_ids_to_tokens    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							={self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab






    def _a					(			self					,   A_    )  ->       List[str]:
         return self.sp_model.encode(A_					,   out_type=A_    )






    def _a					(			self					,   A_    )  ->       str:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id






    def _a					(			self					,   A_    )  ->       int:
         return self.fairseq_ids_to_tokens[index]






    def _a					(			self					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=''.join(A_    ).replace(A_					,   ' '    ).strip()
         return out_string






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
              return
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']					,   )

         if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ) and os.path.isfile(self.vocab_file    ):
              copyfile(self.vocab_file					,   A_    )
         elif not os.path.isfile(self.vocab_file    ):
              with open(A_					,   'wb'    ) as fi:
                   __UpperCamelCase    							=self.sp_model.serialized_model_proto()
                   fi.write(A_    )

         if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
             A_    ) and os.path.isfile(self.monolingual_vocab_file    ):
              copyfile(self.monolingual_vocab_file					,   A_    )
         elif not os.path.isfile(self.monolingual_vocab_file    ):
              with open(A_					,   'w'					,   encoding='utf-8'    ) as fp:
                   for token in self.fairseq_tokens_to_ids:
                        if token not in self.all_special_tokens:
                             fp.write(f'{str(A_    )} \n'    )

         return out_vocab_file, out_monolingual_vocab_file





