"""simple docstring"""



import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CpmAntConfig,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntTokenizer,
				)
@require_torch
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=2      ,			snake_case_=8      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=False      ,			snake_case_=False      ,			snake_case_=False      ,			snake_case_=99      ,			snake_case_=32      ,			snake_case_=3      ,			snake_case_=4      ,			snake_case_=37      ,			snake_case_=32      ,			snake_case_=128      ,			snake_case_=8      ,			snake_case_=8      ,			snake_case_=8      ,			snake_case_=1.0      ,			snake_case_=True      ,			):







										'''simple docstring'''


										A__					:    List[str]														= parent
										A__					:    Optional[Any]														= batch_size
										A__					:    int														= seq_length
										A__					:    Dict														= is_training
										A__					:    Union[str, Any]														= use_token_type_ids
										A__					:    int														= use_input_mask
										A__					:    Dict														= use_labels
										A__					:    List[Any]														= use_mc_token_ids
										A__					:    Tuple														= vocab_size
										A__					:    Any														= hidden_size
										A__					:    Optional[Any]														= num_hidden_layers
										A__					:    int														= num_attention_heads
										A__					:    List[Any]														= intermediate_size
										A__					:    int														= num_buckets
										A__					:    Dict														= max_distance
										A__					:    Union[str, Any]														= prompt_length
										A__					:    Tuple														= prompt_types
										A__					:    Union[str, Any]														= segment_types
										A__					:    int														= init_std
										A__					:    List[str]														= return_dict





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= {}
										A__					:    List[str]														= ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size       ).type(torch.intaa       )
										A__					:    Tuple														= False

										A__					:    Any														= self.get_config()

										return (config, input_ids)





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return CpmAntConfig(
										    vocab_size=self.vocab_size      ,			hidden_size=self.hidden_size      ,			num_hidden_layers=self.num_hidden_layers      ,			num_attention_heads=self.num_attention_heads      ,			dim_ff=self.intermediate_size      ,			position_bias_num_buckets=self.num_buckets      ,			position_bias_max_distance=self.max_distance      ,			prompt_types=self.prompt_types      ,			prompt_length=self.prompt_length      ,			segment_types=self.segment_types      ,			use_cache=snake_case_      ,			init_std=self.init_std      ,			return_dict=self.return_dict      ,			)





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= CpmAntModel(config=snake_case_       )
										model.to(snake_case_       )
										model.eval()

										A__					:    Any														= model(**snake_case_       ).last_hidden_state

										self.parent.assertEqual(hidden_states.shape      ,			(self.batch_size, self.seq_length, config.hidden_size)       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			*snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= CpmAntForCausalLM(snake_case_       )
										model.to(snake_case_       )
										A__					:    str														= input_ids["""input_ids"""].to(snake_case_       )
										model.eval()

										A__					:    Dict														= model(**snake_case_       )
										self.parent.assertEqual(
										    model_output.logits.shape      ,			(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)      ,			)





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Optional[int]														= self.prepare_config_and_inputs()
										return config, inputs_dict





@require_torch
class 	__UpperCAmelCase						(__A       ,      __A       ,      unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	Optional[Any]				    =							(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
			_UpperCamelCase     :	str				    =							(
			    {'feature-extraction': CpmAntModel, 'text-generation': CpmAntForCausalLM} if is_torch_available() else {}
			)

			_UpperCamelCase     :	str				    =							False
			_UpperCamelCase     :	int				    =							False
			_UpperCamelCase     :	List[Any]				    =							False
			_UpperCamelCase     :	Tuple				    =							False
			_UpperCamelCase     :	Any				    =							False





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= CpmAntModelTester(self       )
										A__					:    str														= ConfigTester(self      ,			config_class=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										self.config_tester.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										unittest.skip("""CPMAnt doesn't support input_embeds."""       )(self.test_inputs_embeds       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										unittest.skip(
										    """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""       )(self.test_retain_grad_hidden_states_attentions       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Optional[Any]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_cpmant_model(snake_case_      ,			snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    List[str]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(snake_case_      ,			snake_case_       )





@require_torch
class 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''





			@tooslow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Dict														= """今天天气真好！"""
										A__					:    List[Any]														= """openbmb/cpm-ant-10b"""
										A__					:    int														= CpmAntModel.from_pretrained(snake_case_       )
										A__					:    Union[str, Any]														= CpmAntTokenizer.from_pretrained(snake_case_       )
										A__					:    List[Any]														= tokenizer(snake_case_      ,			return_tensors="""pt"""       )
										A__					:    Optional[int]														= model(**snake_case_       ).last_hidden_state

										A__					:    Dict														= torch.tensor(
										    [[[6.17_08, 5.92_44, 1.08_35], [6.52_07, 6.28_93, -11.33_24], [-1.01_07, -0.05_76, -5.95_77]]]      ,			)
										self.assertTrue(torch.allclose(hidden_states[:, :3, :3]      ,			snake_case_      ,			atol=1E-2       )       )





@require_torch
class 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''





			@tooslow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= """今天天气真好！"""
										A__					:    Dict														= """openbmb/cpm-ant-10b"""
										A__					:    int														= CpmAntForCausalLM.from_pretrained(snake_case_       )
										A__					:    int														= CpmAntTokenizer.from_pretrained(snake_case_       )
										A__					:    Optional[int]														= tokenizer(snake_case_      ,			return_tensors="""pt"""       )
										A__					:    List[Any]														= model(**snake_case_       ).logits

										A__					:    int														= torch.tensor(
										    [[[-6.42_67, -6.40_83, -6.39_58], [-5.88_02, -5.94_47, -5.78_11], [-5.38_96, -5.48_20, -5.42_95]]]      ,			)
										self.assertTrue(torch.allclose(hidden_states[:, :3, :3]      ,			snake_case_      ,			atol=1E-2       )       )





			@tooslow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= """openbmb/cpm-ant-10b"""
										A__					:    List[str]														= CpmAntForCausalLM.from_pretrained(snake_case_       )
										A__					:    Dict														= CpmAntTokenizer.from_pretrained(snake_case_       )
										A__					:    List[Any]														= """今天天气不错，"""
										A__					:    Tuple														= """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
										A__					:    Dict														= tokenizer(snake_case_      ,			return_tensors="""pt"""       )
										A__					:    Dict														= model.generate(**snake_case_       )
										A__					:    Optional[Any]														= tokenizer.batch_decode(snake_case_       )
										self.assertEqual(snake_case_      ,			snake_case_       )





			@tooslow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= """openbmb/cpm-ant-10b"""
										A__					:    List[Any]														= CpmAntForCausalLM.from_pretrained(snake_case_       )
										A__					:    str														= CpmAntTokenizer.from_pretrained(snake_case_       )
										A__					:    Any														= ["""今天天气不错，""", """新年快乐，万事如意！"""]
										A__					:    Union[str, Any]														= [
										    """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
										    """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
										]
										A__					:    Any														= tokenizer(snake_case_      ,			return_tensors="""pt"""      ,			padding=snake_case_       )
										A__					:    List[Any]														= model.generate(**snake_case_       )
										A__					:    Optional[Any]														= tokenizer.batch_decode(snake_case_       )
										self.assertEqual(snake_case_      ,			snake_case_       )






"""simple docstring"""



from dataclasses import dataclass

_UpperCamelCase         =		"0.1"
_UpperCamelCase         =		"Lucia Harcekova"
@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	int
			_UpperCamelCase     :	int
			_UpperCamelCase     :	str





			def __repr__( self       ):







										'''simple docstring'''


										return F'''({self.offset}, {self.length}, {self.indicator})'''





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = 13      ,			snake_case_ = 6       ):







										'''simple docstring'''


										A__					:    str														= window_size
										A__					:    Optional[int]														= lookahead_buffer_size
										A__					:    Optional[Any]														= self.window_size - self.lookahead_buffer_size





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    Any														= """"""

										# while there are still characters in text to compress
										while text:
																	# find the next encoding phrase
																	# - triplet with offset, length, indicator (the next encoding character)
																	A__					:    Optional[Any]														= self._find_encoding_token(snake_case_      ,			snake_case_       )

																	# update the search buffer:
																	# - add new characters from text into it
																	# - check if size exceed the max search buffer size, if so, drop the
																	#   oldest elements
																	search_buffer += text[: token.length + 1]
																	if len(snake_case_       ) > self.search_buffer_size:
																								A__					:    Any														= search_buffer[-self.search_buffer_size :]

																	# update the text
																	A__					:    Optional[Any]														= text[token.length + 1 :]

																	# append the token to output
																	output.append(snake_case_       )

										return output





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= """"""

										for token in tokens:
																	for _ in range(token.length       ):
																								output += output[-token.offset]
																	output += token.indicator

										return output





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not text:
																	raise ValueError("""We need some text to work with."""       )

										# Initialise result parameters to default values
										A__							,       A__					:    List[Any]														= 0, 0

										if not search_buffer:
																	return Token(snake_case_      ,			snake_case_      ,			text[length]       )

										for i, character in enumerate(snake_case_       ):
																	A__					:    Tuple														= len(snake_case_       ) - i
																	if character == text[0]:
																								A__					:    Tuple														= self._match_length_from_index(snake_case_      ,			snake_case_      ,			0      ,			snake_case_       )
																								# if the found length is bigger than the current or if it's equal,
																								# which means it's offset is smaller: update offset and length
																								if found_length >= length:
																															A__							,       A__					:    List[str]														= found_offset, found_length

										return Token(snake_case_      ,			snake_case_      ,			text[length]       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not text or text[text_index] != window[window_index]:
																	return 0
										return 1 + self._match_length_from_index(
										    snake_case_      ,			window + text[text_index]      ,			text_index + 1      ,			window_index + 1       )


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				# Initialize compressor class
				_UpperCamelCase         =		LZaaCompressor(window_size=13, lookahead_buffer_size=6)

				# Example
				_UpperCamelCase         =		"cabracadabrarrarrad"
				_UpperCamelCase         =		lzaa_compressor.compress(TEXT)
				print(lzaa_compressor.compress("ababcbababaa"))
				_UpperCamelCase         =		lzaa_compressor.decompress(compressed_text)
				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."





