



import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 							lowerCAmelCase_						(    a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   List[Any]  	=    CTRLTokenizer
       UpperCAmelCase__							:   int  	=    False
       UpperCAmelCase__							:   Tuple  	=    False
       def    snake_case_			(     self			)	->						int:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCamelCase       :    Optional[Any]		      =      ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
          UpperCamelCase       :    Any		      =      dict(zip(SCREAMING_SNAKE_CASE_,     range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
          UpperCamelCase       :    Union[str, Any]		      =      ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
          UpperCamelCase       :    List[Any]		      =      {'unk_token': '<unk>'}

          UpperCamelCase       :    Optional[Any]		      =      os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['vocab_file']			)
          UpperCamelCase       :    Any		      =      os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['merges_file']			)
          with open(self.vocab_file,     'w',     encoding='utf-8'			) as fp:
             fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '\n'			)
          with open(self.merges_file,     'w',     encoding='utf-8'			) as fp:
             fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)
       def    snake_case_			(     self,     **SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          kwargs.update(self.special_tokens_map			)
          return CTRLTokenizer.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Any		      =      'adapt react readapt apt'
          UpperCamelCase       :    Optional[Any]		      =      'adapt react readapt apt'
          return input_text, output_text






       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    str		      =      CTRLTokenizer(self.vocab_file,     self.merges_file,     **self.special_tokens_map			)
          UpperCamelCase       :    Union[str, Any]		      =      'adapt react readapt apt'
          UpperCamelCase       :    int		      =      'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
          UpperCamelCase       :    List[str]		      =      tokenizer.tokenize(SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Tuple		      =      tokens + [tokenizer.unk_token]

          UpperCamelCase       :    Any		      =      [0, 1, 2, 4, 5, 1, 0, 3, 6]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			),     SCREAMING_SNAKE_CASE_			)








"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



