


'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'bart'
		_a     =				['past_key_values']
		_a     =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




		def __init__(		self    ,   UpperCamelCase_=50265    ,   UpperCamelCase_=1024    ,   UpperCamelCase_=12    ,   UpperCamelCase_=4096    ,   UpperCamelCase_=16    ,   UpperCamelCase_=12    ,   UpperCamelCase_=4096    ,   UpperCamelCase_=16    ,   UpperCamelCase_=0.0    ,   UpperCamelCase_=0.0    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=1024    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.0    ,   UpperCamelCase_=0.0    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=0.0    ,   UpperCamelCase_=False    ,   UpperCamelCase_=True    ,   UpperCamelCase_=3    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_=True    ,   UpperCamelCase_=2    ,   UpperCamelCase_=2    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :int												= vocab_size
							UpperCamelCase__   :List[str]												= max_position_embeddings
							UpperCamelCase__   :int												= d_model
							UpperCamelCase__   :Tuple												= encoder_ffn_dim
							UpperCamelCase__   :List[Any]												= encoder_layers
							UpperCamelCase__   :int												= encoder_attention_heads
							UpperCamelCase__   :Optional[Any]												= decoder_ffn_dim
							UpperCamelCase__   :Optional[int]												= decoder_layers
							UpperCamelCase__   :List[str]												= decoder_attention_heads
							UpperCamelCase__   :List[Any]												= dropout
							UpperCamelCase__   :Dict												= attention_dropout
							UpperCamelCase__   :Any												= activation_dropout
							UpperCamelCase__   :int												= activation_function
							UpperCamelCase__   :List[str]												= init_std
							UpperCamelCase__   :Optional[Any]												= encoder_layerdrop
							UpperCamelCase__   :int												= decoder_layerdrop
							UpperCamelCase__   :str												= classifier_dropout
							UpperCamelCase__   :str												= use_cache
							UpperCamelCase__   :Union[str, Any]												= encoder_layers
							UpperCamelCase__   :Optional[Any]												= scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    num_labels=UpperCamelCase_    ,   pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_    ,   is_encoder_decoder=UpperCamelCase_    ,   decoder_start_token_id=UpperCamelCase_    ,   forced_eos_token_id=UpperCamelCase_    ,   **UpperCamelCase_    ,   )

							# ensure backward compatibility for BART CNN models
							if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''    ,   UpperCamelCase_							):
												UpperCamelCase__   :Optional[int]												= self.bos_token_id
												warnings.warn(
												    F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
												    '''The config can simply be saved and uploaded again to be fixed.'''							)



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__   :Dict												= OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]							)

												if self.use_past:
																	UpperCamelCase__   :Optional[Any]												= {0: '''batch'''}
																	UpperCamelCase__   :int												= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																	UpperCamelCase__   :List[Any]												= {0: '''batch''', 1: '''decoder_sequence'''}
																	UpperCamelCase__   :int												= {0: '''batch''', 1: '''decoder_sequence'''}

												if self.use_past:
																	self.fill_with_past_key_values_(UpperCamelCase_    ,   direction='''inputs'''							)
							elif self.task == "causal-lm":
												# TODO: figure this case out.
												UpperCamelCase__   :List[str]												= OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]							)
												if self.use_past:
																	UpperCamelCase__					,				UpperCamelCase__   :Optional[Any]												= self.num_layers
																	for i in range(UpperCamelCase_							):
																						UpperCamelCase__   :Tuple												= {0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__   :List[str]												= {0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
												UpperCamelCase__   :int												= OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
												        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
												    ]							)

							return common_inputs




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__   :Optional[Any]												= super().outputs
							else:
												UpperCamelCase__   :List[str]												= super(UpperCamelCase_    ,   self							).outputs
												if self.use_past:
																	UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self.num_layers
																	for i in range(UpperCamelCase_							):
																						UpperCamelCase__   :Any												= {0: '''batch''', 2: '''past_sequence + sequence'''}
																						UpperCamelCase__   :Union[str, Any]												= {0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							# Generate decoder inputs
							UpperCamelCase__   :Optional[Any]												= seq_length if not self.use_past else 1
							UpperCamelCase__   :Optional[Any]												= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							UpperCamelCase__   :int												= dict(**UpperCamelCase_    ,   **UpperCamelCase_							)

							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
												else:
																	import torch
												UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= common_inputs['''input_ids'''].shape
												UpperCamelCase__   :Optional[int]												= common_inputs['''decoder_input_ids'''].shape[1]
												UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self.num_attention_heads
												UpperCamelCase__   :Tuple												= (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												UpperCamelCase__   :Any												= decoder_seq_length + 3
												UpperCamelCase__   :str												= (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												UpperCamelCase__   :str												= torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_    ,   UpperCamelCase_							)]    ,   dim=1							)

												UpperCamelCase__   :Union[str, Any]												= []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												UpperCamelCase__					,				UpperCamelCase__   :Any												= self.num_layers
												UpperCamelCase__   :Optional[int]												= min(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :Any												= max(UpperCamelCase_    ,   UpperCamelCase_							) - min_num_layers
												UpperCamelCase__   :Tuple												= '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

												for _ in range(UpperCamelCase_							):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(UpperCamelCase_							),
																	        torch.zeros(UpperCamelCase_							),
																	        torch.zeros(UpperCamelCase_							),
																	        torch.zeros(UpperCamelCase_							),
																	    )							)
												# TODO: test this.
												UpperCamelCase__   :Any												= encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
												for _ in range(UpperCamelCase_    ,   UpperCamelCase_							):
																	common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_							), torch.zeros(UpperCamelCase_							))							)
							return common_inputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
												else:
																	import torch
												UpperCamelCase__					,				UpperCamelCase__   :List[str]												= common_inputs['''input_ids'''].shape
												# Not using the same length for past_key_values
												UpperCamelCase__   :List[str]												= seqlen + 2
												UpperCamelCase__					,				UpperCamelCase__   :Tuple												= self.num_layers
												UpperCamelCase__					,				UpperCamelCase__   :Any												= self.num_attention_heads
												UpperCamelCase__   :List[Any]												= (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												UpperCamelCase__   :Optional[Any]												= common_inputs['''attention_mask'''].dtype
												UpperCamelCase__   :int												= torch.cat(
												    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_    ,   UpperCamelCase_    ,   dtype=UpperCamelCase_							)]    ,   dim=1							)
												UpperCamelCase__   :Any												= [
												    (torch.zeros(UpperCamelCase_							), torch.zeros(UpperCamelCase_							)) for _ in range(UpperCamelCase_							)
												]
							return common_inputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= compute_effective_axis_dimension(
							    UpperCamelCase_    ,   fixed_dimension=OnnxConfig.default_fixed_batch    ,   num_token_to_add=0							)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCamelCase__   :Optional[Any]												= tokenizer.num_special_tokens_to_add(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= compute_effective_axis_dimension(
							    UpperCamelCase_    ,   fixed_dimension=OnnxConfig.default_fixed_sequence    ,   num_token_to_add=UpperCamelCase_							)

							# Generate dummy inputs according to compute batch and sequence
							UpperCamelCase__   :Tuple												= [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
							UpperCamelCase__   :str												= dict(tokenizer(UpperCamelCase_    ,   return_tensors=UpperCamelCase_							)							)
							return common_inputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = -1    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__   :Dict												= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    UpperCamelCase_    ,   batch_size=UpperCamelCase_    ,   seq_length=UpperCamelCase_    ,   is_pair=UpperCamelCase_    ,   framework=UpperCamelCase_							)

							elif self.task == "causal-lm":
												UpperCamelCase__   :Any												= self._generate_dummy_inputs_for_causal_lm(
												    UpperCamelCase_    ,   batch_size=UpperCamelCase_    ,   seq_length=UpperCamelCase_    ,   is_pair=UpperCamelCase_    ,   framework=UpperCamelCase_							)
							else:
												UpperCamelCase__   :Union[str, Any]												= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    UpperCamelCase_    ,   batch_size=UpperCamelCase_    ,   seq_length=UpperCamelCase_    ,   is_pair=UpperCamelCase_    ,   framework=UpperCamelCase_							)

							return common_inputs




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							if self.task in ["default", "seq2seq-lm"]:
												UpperCamelCase__   :List[Any]												= super()._flatten_past_key_values_(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							else:
												UpperCamelCase__   :Any												= super(UpperCamelCase_    ,   self							)._flatten_past_key_values_(
												    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
'''simple docstring'''

import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

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

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					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(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

