






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 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      int			 =XGLMConfig
				UpperCamelCase__ :      Optional[Any]			 ={}
				UpperCamelCase__ :      List[str]			 ="""gelu"""


				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__=14							,  lowerCamelCase__=7							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=99							,  lowerCamelCase__=32							,  lowerCamelCase__=2							,  lowerCamelCase__=4							,  lowerCamelCase__=37							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=512							,  lowerCamelCase__=0.02							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=parent
									__UpperCamelCase      :					List[str]       		=batch_size
									__UpperCamelCase      :					str       		=seq_length
									__UpperCamelCase      :					Dict       		=is_training
									__UpperCamelCase      :					Tuple       		=use_input_mask
									__UpperCamelCase      :					List[Any]       		=use_labels
									__UpperCamelCase      :					Any       		=vocab_size
									__UpperCamelCase      :					List[Any]       		=d_model
									__UpperCamelCase      :					Optional[int]       		=num_hidden_layers
									__UpperCamelCase      :					List[str]       		=num_attention_heads
									__UpperCamelCase      :					Optional[int]       		=ffn_dim
									__UpperCamelCase      :					str       		=activation_function
									__UpperCamelCase      :					Any       		=activation_dropout
									__UpperCamelCase      :					Optional[int]       		=attention_dropout
									__UpperCamelCase      :					Optional[int]       		=max_position_embeddings
									__UpperCamelCase      :					Any       		=initializer_range
									__UpperCamelCase      :					Dict       		=None
									__UpperCamelCase      :					Optional[int]       		=0
									__UpperCamelCase      :					Optional[Any]       		=2
									__UpperCamelCase      :					str       		=1


				def       __lowercase							( self    ):




									"""simple docstring"""

									return XGLMConfig.from_pretrained('facebook/xglm-564M'    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=tf.clip_by_value(
									    ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size    )							,  clip_value_min=0							,  clip_value_max=3    )

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

									__UpperCamelCase      :					Any       		=self.get_config()

									__UpperCamelCase      :					Optional[Any]       		=floats_tensor([self.num_hidden_layers, self.num_attention_heads]							,  2    )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)


				def       __lowercase							( self    ):




									"""simple docstring"""

									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=lowerCamelCase__							,  bos_token_id=self.bos_token_id							,  eos_token_id=self.eos_token_id							,  pad_token_id=self.pad_token_id							,  return_dict=lowerCamelCase__							,  )







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=self.prepare_config_and_inputs()

									(
									    (
									    __UpperCamelCase
									)		,				(
									    __UpperCamelCase
									)		,				(
									    __UpperCamelCase
									)		,				(
									    __UpperCamelCase
									)		,				
									)      :					int       		=config_and_inputs

									__UpperCamelCase      :					Optional[Any]       		={
									    'input_ids': input_ids,
									    'head_mask': head_mask,
									}

									return config, inputs_dict




@require_tf
class 				__A     (			a      ,     a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Union[str, Any]			 =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				UpperCamelCase__ :      str			 =(TFXGLMForCausalLM,) if is_tf_available() else ()
				UpperCamelCase__ :      Optional[Any]			 =(
				    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				UpperCamelCase__ :      Tuple			 =False
				UpperCamelCase__ :      Tuple			 =False
				UpperCamelCase__ :      Optional[Any]			 =False


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=TFXGLMModelTester(self    )
									__UpperCamelCase      :					Dict       		=ConfigTester(self							,  config_class=lowerCamelCase__							,  n_embd=37    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									self.config_tester.run_common_tests()


				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__UpperCamelCase      :					Optional[Any]       		=TFXGLMModel.from_pretrained(lowerCamelCase__    )
														self.assertIsNotNone(lowerCamelCase__    )







				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'    )
				def       __lowercase							( self    ):




									"""simple docstring"""

									super().test_resize_token_embeddings()




@require_tf
class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				@slow
				def       __lowercase							( self							,  lowerCamelCase__=True    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
									__UpperCamelCase      :					List[str]       		=tf.convert_to_tensor([[2, 268, 9865]]							,  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
									__UpperCamelCase      :					str       		=[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
									# fmt: on
									__UpperCamelCase      :					Optional[Any]       		=model.generate(lowerCamelCase__							,  do_sample=lowerCamelCase__							,  num_beams=1    )
									if verify_outputs:
														self.assertListEqual(output_ids[0].numpy().tolist()							,  lowerCamelCase__    )


				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )
									__UpperCamelCase      :					Union[str, Any]       		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )

									tf.random.set_seed(0    )
									__UpperCamelCase      :					str       		=tokenizer('Today is a nice day and'							,  return_tensors='tf'    )
									__UpperCamelCase      :					Union[str, Any]       		=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'    ):
														__UpperCamelCase      :					Any       		=model.generate(lowerCamelCase__							,  do_sample=lowerCamelCase__							,  seed=[7, 0]    )
									__UpperCamelCase      :					Tuple       		=tokenizer.decode(output_ids[0]							,  skip_special_tokens=lowerCamelCase__    )

									__UpperCamelCase      :					List[Any]       		=(
									    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
									)
									self.assertEqual(lowerCamelCase__							,  lowerCamelCase__    )







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
									__UpperCamelCase      :					Optional[Any]       		=XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )

									__UpperCamelCase      :					Optional[Any]       		='left'

									# use different length sentences to test batching
									__UpperCamelCase      :					Optional[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',
									]

									__UpperCamelCase      :					List[Any]       		=tokenizer(lowerCamelCase__							,  return_tensors='tf'							,  padding=lowerCamelCase__    )
									__UpperCamelCase      :					Union[str, Any]       		=inputs['input_ids']

									__UpperCamelCase      :					Dict       		=model.generate(input_ids=lowerCamelCase__							,  attention_mask=inputs['attention_mask']							,  max_new_tokens=12    )

									__UpperCamelCase      :					List[Any]       		=tokenizer(sentences[0]							,  return_tensors='tf'    ).input_ids
									__UpperCamelCase      :					Dict       		=model.generate(input_ids=lowerCamelCase__							,  max_new_tokens=12    )

									__UpperCamelCase      :					Any       		=tokenizer(sentences[1]							,  return_tensors='tf'    ).input_ids
									__UpperCamelCase      :					Optional[Any]       		=model.generate(input_ids=lowerCamelCase__							,  max_new_tokens=12    )

									__UpperCamelCase      :					Optional[int]       		=tokenizer.batch_decode(lowerCamelCase__							,  skip_special_tokens=lowerCamelCase__    )
									__UpperCamelCase      :					Union[str, Any]       		=tokenizer.decode(output_non_padded[0]							,  skip_special_tokens=lowerCamelCase__    )
									__UpperCamelCase      :					int       		=tokenizer.decode(output_padded[0]							,  skip_special_tokens=lowerCamelCase__    )

									__UpperCamelCase      :					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(lowerCamelCase__							,  lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__							,  [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


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

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} 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							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					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 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

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

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
