

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 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[Any]             =       XGLMConfig
							UpperCAmelCase__			:		int             =       {}
							UpperCAmelCase__			:		Optional[Any]             =       "gelu"


							def __init__(     self			,				_a			,				_a=1_4			,				_a=7			,				_a=True			,				_a=True			,				_a=True			,				_a=9_9			,				_a=3_2			,				_a=2			,				_a=4			,				_a=3_7			,				_a="gelu"			,				_a=0.1			,				_a=0.1			,				_a=5_1_2			,				_a=0.02			,				)       ->       Dict:
												_a      :       List[str]   =						parent
												_a      :       Optional[Any]   =						batch_size
												_a      :       Dict   =						seq_length
												_a      :       Tuple   =						is_training
												_a      :       List[str]   =						use_input_mask
												_a      :       Optional[Any]   =						use_labels
												_a      :       Dict   =						vocab_size
												_a      :       List[Any]   =						d_model
												_a      :       Any   =						num_hidden_layers
												_a      :       List[Any]   =						num_attention_heads
												_a      :       Tuple   =						ffn_dim
												_a      :       Any   =						activation_function
												_a      :       List[str]   =						activation_dropout
												_a      :       Tuple   =						attention_dropout
												_a      :       Union[str, Any]   =						max_position_embeddings
												_a      :       int   =						initializer_range
												_a      :       Union[str, Any]   =						None
												_a      :       Any   =						0
												_a      :       Tuple   =						2
												_a      :       Union[str, Any]   =						1


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return XGLMConfig.from_pretrained('''facebook/xglm-564M'''			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Dict   =						tf.clip_by_value(
												    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)			,				clip_value_min=0			,				clip_value_max=3			)

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

												_a      :       Dict   =						self.get_config()

												_a      :       Tuple   =						floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,				2			)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)


							def 							__lowercase							(     self			)       ->       int:
												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 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.prepare_config_and_inputs()

												(
												    (
												    _a
												) ,   (
												    _a
												) ,   (
												    _a
												) ,   (
												    _a
												) ,   
												)      :       Dict   =						config_and_inputs

												_a      :       List[str]   =						{
												    '''input_ids''': input_ids,
												    '''head_mask''': head_mask,
												}

												return config, inputs_dict



@require_tf
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Dict             =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
							UpperCAmelCase__			:		Optional[Any]             =       (TFXGLMForCausalLM,) if is_tf_available() else ()
							UpperCAmelCase__			:		Tuple             =       (
							    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
							)
							UpperCAmelCase__			:		Optional[int]             =       False
							UpperCAmelCase__			:		Any             =       False
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       str:
												_a      :       str   =						TFXGLMModelTester(self			)
												_a      :       List[str]   =						ConfigTester(self			,				config_class=_a			,				n_embd=3_7			)


							def 							__lowercase							(     self			)       ->       Tuple:
												self.config_tester.run_common_tests()


							@slow
							def 							__lowercase							(     self			)       ->       List[str]:
												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Union[str, Any]   =						TFXGLMModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)




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



@require_tf
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@slow
							def 							__lowercase							(     self			,				_a=True			)       ->       Dict:
												_a      :       Any   =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)
												_a      :       Any   =						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]			,				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
												_a      :       Union[str, Any]   =						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
												# fmt: on
												_a      :       Any   =						model.generate(_a			,				do_sample=_a			,				num_beams=1			)
												if verify_outputs:
																	self.assertListEqual(output_ids[0].numpy().tolist()			,				_a			)


							@slow
							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       List[Any]   =						XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''			)
												_a      :       Optional[int]   =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)

												tf.random.set_seed(0			)
												_a      :       Optional[Any]   =						tokenizer('''Today is a nice day and'''			,				return_tensors='''tf'''			)
												_a      :       Optional[int]   =						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'''			):
																	_a      :       List[str]   =						model.generate(_a			,				do_sample=_a			,				seed=[7, 0]			)
												_a      :       Dict   =						tokenizer.decode(output_ids[0]			,				skip_special_tokens=_a			)

												_a      :       int   =						(
												    '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
												)
												self.assertEqual(_a			,				_a			)




							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''			)
												_a      :       Union[str, Any]   =						XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''			)

												_a      :       Tuple   =						'''left'''

												# use different length sentences to test batching
												_a      :       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''',
												]

												_a      :       Optional[int]   =						tokenizer(_a			,				return_tensors='''tf'''			,				padding=_a			)
												_a      :       int   =						inputs['''input_ids''']

												_a      :       Optional[Any]   =						model.generate(input_ids=_a			,				attention_mask=inputs['''attention_mask''']			,				max_new_tokens=1_2			)

												_a      :       Optional[int]   =						tokenizer(sentences[0]			,				return_tensors='''tf'''			).input_ids
												_a      :       Optional[Any]   =						model.generate(input_ids=_a			,				max_new_tokens=1_2			)

												_a      :       Dict   =						tokenizer(sentences[1]			,				return_tensors='''tf'''			).input_ids
												_a      :       int   =						model.generate(input_ids=_a			,				max_new_tokens=1_2			)

												_a      :       Union[str, Any]   =						tokenizer.batch_decode(_a			,				skip_special_tokens=_a			)
												_a      :       str   =						tokenizer.decode(output_non_padded[0]			,				skip_special_tokens=_a			)
												_a      :       Dict   =						tokenizer.decode(output_padded[0]			,				skip_special_tokens=_a			)

												_a      :       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]			)




'''simple docstring'''







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

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


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCamelCase_      	=	None


UpperCamelCase_      	=	logging.get_logger(__name__)


UpperCamelCase_      	=	'''▁'''

UpperCamelCase_      	=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_      	=	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase_      	=	{
    '''google/pegasus-xsum''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							str     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							int     		= PegasusTokenizer
						A_			:							Dict     		= ['input_ids', 'attention_mask']




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Union[str, Any]="<pad>"							,							UpperCamelCase_ :       List[Any]="</s>"							,							UpperCamelCase_ :       Tuple="<unk>"							,							UpperCamelCase_ :       List[Any]="<mask_2>"							,							UpperCamelCase_ :       int="<mask_1>"							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[Any]=1_03							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = offset

													if additional_special_tokens is not None:
																				if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											raise TypeError(
																											    f'''additional_special_tokens should be of type {type(UpperCamelCase_      )}, but is'''
																											    f''' {type(UpperCamelCase_      )}'''      )

																				SCREAMING_SNAKE_CASE__     :List[str]						    = (
																				    ([mask_token_sent] + additional_special_tokens)
																				    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																				    else additional_special_tokens
																				)
																				# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																				additional_special_tokens_extended += [
																				    f'''<unk_{i}>''' for i in range(len(UpperCamelCase_      )							,							self.offset - 1      )
																				]

																				if len(set(UpperCamelCase_      )      ) != len(UpperCamelCase_      ):
																											raise ValueError(
																											    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = additional_special_tokens_extended
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [mask_token_sent] if mask_token_sent is not None else []
																				additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset      )]

													super().__init__(
													    UpperCamelCase_							,							tokenizer_file=UpperCamelCase_							,							pad_token=UpperCamelCase_							,							eos_token=UpperCamelCase_							,							unk_token=UpperCamelCase_							,							mask_token=UpperCamelCase_							,							mask_token_sent=UpperCamelCase_							,							offset=UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = set(self.all_special_ids      )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
																				raise ValueError(
																				    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
																				    f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

													return [1 if x in all_special_ids else 0 for x in seq]




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List							,							UpperCamelCase_ :       Optional[List] = None							,							UpperCamelCase_ :       bool = False      )     ->				List[int]:
													if already_has_special_tokens:
																				return self._special_token_mask(UpperCamelCase_      )
													elif token_ids_a is None:
																				return self._special_token_mask(UpperCamelCase_      ) + [1]
													else:
																				return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict=None      )     ->				List[int]:
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													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
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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,)






