






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


A_		:		Optional[Any]		     =			logging.get_logger(__name__)

A_		:		List[str]		     =			{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[int]									='''marian'''
       a    :		Dict									=['''past_key_values''']
       a    :		Any									={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__( self  ,      _lowerCamelCase=5_8_1_0_1  ,      _lowerCamelCase=None  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=5_8_1_0_0  ,      _lowerCamelCase=False  ,      _lowerCamelCase=5_8_1_0_0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=True  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: int   				=    vocab_size
         UpperCamelCase_: Any   				=    decoder_vocab_size or vocab_size
         UpperCamelCase_: List[str]   				=    max_position_embeddings
         UpperCamelCase_: Optional[Any]   				=    d_model
         UpperCamelCase_: Optional[Any]   				=    encoder_ffn_dim
         UpperCamelCase_: List[Any]   				=    encoder_layers
         UpperCamelCase_: List[Any]   				=    encoder_attention_heads
         UpperCamelCase_: Dict   				=    decoder_ffn_dim
         UpperCamelCase_: Any   				=    decoder_layers
         UpperCamelCase_: Optional[int]   				=    decoder_attention_heads
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: List[str]   				=    attention_dropout
         UpperCamelCase_: List[str]   				=    activation_dropout
         UpperCamelCase_: Union[str, Any]   				=    activation_function
         UpperCamelCase_: List[Any]   				=    init_std
         UpperCamelCase_: Union[str, Any]   				=    encoder_layerdrop
         UpperCamelCase_: Optional[int]   				=    decoder_layerdrop
         UpperCamelCase_: List[str]   				=    use_cache
         UpperCamelCase_: List[Any]   				=    encoder_layers
         UpperCamelCase_: str   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      is_encoder_decoder=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      forced_eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def       _a   ( self							):
         if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase_: List[str]   				=    OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ]							)

           if self.use_past:
             UpperCamelCase_: int   				=    {0: 'batch'}
             UpperCamelCase_: Tuple   				=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
             UpperCamelCase_: Tuple   				=    {0: 'batch', 1: 'decoder_sequence'}
             UpperCamelCase_: Any   				=    {0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
             self.fill_with_past_key_values_(_lowerCamelCase  ,      direction='inputs'							)
         elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase_: Optional[int]   				=    OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
               ]							)
           if self.use_past:
             UpperCamelCase_				,UpperCamelCase_: List[str]   				=    self.num_layers
             for i in range(_lowerCamelCase							):
               UpperCamelCase_: str   				=    {0: 'batch', 2: 'past_sequence + sequence'}
               UpperCamelCase_: str   				=    {0: 'batch', 2: 'past_sequence + sequence'}
         else:
           UpperCamelCase_: Union[str, Any]   				=    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
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
       def       _a   ( self							):
         if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase_: Optional[Any]   				=    super().outputs
         else:
           UpperCamelCase_: int   				=    super(_lowerCamelCase  ,      self							).outputs
           if self.use_past:
             UpperCamelCase_				,UpperCamelCase_: str   				=    self.num_layers
             for i in range(_lowerCamelCase							):
               UpperCamelCase_: Dict   				=    {0: 'batch', 2: 'past_sequence + sequence'}
               UpperCamelCase_: Union[str, Any]   				=    {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      ):
         UpperCamelCase_: Tuple   				=    self._generate_dummy_inputs_for_encoder_and_decoder(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)

         # Generate decoder inputs
         UpperCamelCase_: Any   				=    seq_length if not self.use_past else 1
         UpperCamelCase_: Union[str, Any]   				=    self._generate_dummy_inputs_for_encoder_and_decoder(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: Dict   				=    {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_: Any   				=    dict(**_lowerCamelCase  ,      **_lowerCamelCase							)

         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_: Dict   				=    common_inputs['decoder_input_ids'].shape[1]
           UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    self.num_attention_heads
           UpperCamelCase_: Union[str, Any]   				=    (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase_: Dict   				=    decoder_seq_length + 3
           UpperCamelCase_: List[Any]   				=    (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

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

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

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      ):
         UpperCamelCase_: List[str]   				=    self._generate_dummy_inputs_for_encoder_and_decoder(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)

         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_: Union[str, Any]   				=    common_inputs['input_ids'].shape
           # Not using the same length for past_key_values
           UpperCamelCase_: List[Any]   				=    seqlen + 2
           UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.num_layers
           UpperCamelCase_				,UpperCamelCase_: List[str]   				=    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[int]   				=    common_inputs['attention_mask'].dtype
           UpperCamelCase_: Union[str, Any]   				=    torch.cat(
               [common_inputs['attention_mask'], torch.ones(_lowerCamelCase  ,      _lowerCamelCase  ,      dtype=_lowerCamelCase							)]  ,      dim=1							)
           UpperCamelCase_: int   				=    [
               (torch.zeros(_lowerCamelCase							), torch.zeros(_lowerCamelCase							)) for _ in range(_lowerCamelCase							)
           ]
         return common_inputs





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      ):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCamelCase_: List[str]   				=    compute_effective_axis_dimension(
             _lowerCamelCase  ,      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_: int   				=    tokenizer.num_special_tokens_to_add(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    compute_effective_axis_dimension(
             _lowerCamelCase  ,      fixed_dimension=OnnxConfig.default_fixed_sequence  ,      num_token_to_add=_lowerCamelCase							)

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      ):
         if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase_: List[Any]   				=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               _lowerCamelCase  ,      batch_size=_lowerCamelCase  ,      seq_length=_lowerCamelCase  ,      is_pair=_lowerCamelCase  ,      framework=_lowerCamelCase							)

         else:
           UpperCamelCase_: int   				=    self._generate_dummy_inputs_for_causal_lm(
               _lowerCamelCase  ,      batch_size=_lowerCamelCase  ,      seq_length=_lowerCamelCase  ,      is_pair=_lowerCamelCase  ,      framework=_lowerCamelCase							)

         return common_inputs





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase_: Dict   				=    super()._flatten_past_key_values_(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)
         else:
           UpperCamelCase_: Optional[int]   				=    super(_lowerCamelCase  ,      self							)._flatten_past_key_values_(
               _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       @property
       def       _a   ( self							):
         return 1e-4







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass