






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


UpperCamelCase											=      logging.get_logger(__name__)

UpperCamelCase											=      {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class 	lowerCamelCase__				(							UpperCAmelCase ):
      lowerCamelCase_							:   List[str]											=	'mbart'
      lowerCamelCase_							:   str											=	['past_key_values']
      lowerCamelCase_							:   Dict											=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






      def __init__(self					:    Optional[int]		,		_snake_case					:    str=5_0265		,		_snake_case					:    Optional[int]=1024		,		_snake_case					:    Optional[int]=12		,		_snake_case					:    str=4096		,		_snake_case					:    Union[str, Any]=16		,		_snake_case					:    Optional[int]=12		,		_snake_case					:    Tuple=4096		,		_snake_case					:    int=16		,		_snake_case					:    List[str]=0.0		,		_snake_case					:    Union[str, Any]=0.0		,		_snake_case					:    Union[str, Any]=True		,		_snake_case					:    Optional[int]=True		,		_snake_case					:    List[str]="gelu"		,		_snake_case					:    Optional[int]=1024		,		_snake_case					:    Union[str, Any]=0.1		,		_snake_case					:    Optional[int]=0.0		,		_snake_case					:    Dict=0.0		,		_snake_case					:    Dict=0.02		,		_snake_case					:    List[str]=0.0		,		_snake_case					:    Optional[Any]=False		,		_snake_case					:    List[Any]=1		,		_snake_case					:    List[str]=0		,		_snake_case					:    int=2		,		_snake_case					:    Dict=2		,		**_snake_case					:    Optional[Any]		,		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     vocab_size
       lowerCamelCase_							:    str							=     max_position_embeddings
       lowerCamelCase_							:    Dict							=     d_model
       lowerCamelCase_							:    Optional[int]							=     encoder_ffn_dim
       lowerCamelCase_							:    Union[str, Any]							=     encoder_layers
       lowerCamelCase_							:    Optional[int]							=     encoder_attention_heads
       lowerCamelCase_							:    Optional[int]							=     decoder_ffn_dim
       lowerCamelCase_							:    List[str]							=     decoder_layers
       lowerCamelCase_							:    List[Any]							=     decoder_attention_heads
       lowerCamelCase_							:    Optional[int]							=     dropout
       lowerCamelCase_							:    List[Any]							=     attention_dropout
       lowerCamelCase_							:    str							=     activation_dropout
       lowerCamelCase_							:    Dict							=     activation_function
       lowerCamelCase_							:    Tuple							=     init_std
       lowerCamelCase_							:    Dict							=     encoder_layerdrop
       lowerCamelCase_							:    Any							=     decoder_layerdrop
       lowerCamelCase_							:    Optional[Any]							=     classifier_dropout
       lowerCamelCase_							:    List[str]							=     use_cache
       lowerCamelCase_							:    Optional[Any]							=     encoder_layers
       lowerCamelCase_							:    List[Any]							=     scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=_snake_case		,		bos_token_id=_snake_case		,		eos_token_id=_snake_case		,		is_encoder_decoder=_snake_case		,		forced_eos_token_id=_snake_case		,		**_snake_case		,		)




class 	lowerCamelCase__				(							UpperCAmelCase ):






      @property
      def      UpperCAmelCase_     (self					:    Any		)       ->   Mapping[str, Mapping[int, str]]:


       """simple docstring"""






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

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

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


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Union[str, Any]		,		_snake_case					:    PreTrainedTokenizer		,		_snake_case					:    int = -1		,		_snake_case					:    int = -1		,		_snake_case					:    bool = False		,		_snake_case					:    Optional[TensorType] = None		,		)       ->   Mapping[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)

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

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

        lowerCamelCase_							:    Tuple							=     torch.cat(
            [common_inputs['decoder_attention_mask'], torch.ones(_snake_case		,		_snake_case		)]		,		dim=1		)

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

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






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    PreTrainedTokenizer		,		_snake_case					:    int = -1		,		_snake_case					:    int = -1		,		_snake_case					:    bool = False		,		_snake_case					:    Optional[TensorType] = None		,		)       ->   Mapping[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)

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

        lowerCamelCase_							:    Dict							=     common_inputs['attention_mask'].dtype
        lowerCamelCase_							:    List[str]							=     torch.cat(
            [common_inputs['attention_mask'], torch.ones(_snake_case		,		_snake_case		,		dtype=_snake_case		)]		,		dim=1		)
        lowerCamelCase_							:    int							=     [
            (torch.zeros(_snake_case		), torch.zeros(_snake_case		)) for _ in range(_snake_case		)
        ]
       return common_inputs






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    PreTrainedTokenizer		,		_snake_case					:    int = -1		,		_snake_case					:    int = -1		,		_snake_case					:    bool = False		,		_snake_case					:    Optional[TensorType] = None		,		)       ->   Mapping[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     compute_effective_axis_dimension(
           _snake_case		,		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
       lowerCamelCase_							:    Dict							=     tokenizer.num_special_tokens_to_add(_snake_case		)
       lowerCamelCase_							:    str							=     compute_effective_axis_dimension(
           _snake_case		,		fixed_dimension=OnnxConfig.default_fixed_sequence		,		num_token_to_add=_snake_case		)

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






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    PreTrainedTokenizer		,		_snake_case					:    int = -1		,		_snake_case					:    int = -1		,		_snake_case					:    bool = False		,		_snake_case					:    Optional[TensorType] = None		,		)       ->   Mapping[str, Any]:


       """simple docstring"""






       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase_							:    Union[str, Any]							=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            _snake_case		,		batch_size=_snake_case		,		seq_length=_snake_case		,		is_pair=_snake_case		,		framework=_snake_case		)

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

       return common_inputs






      def      UpperCAmelCase_     (self					:    Optional[int]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    str		,		_snake_case					:    List[Any]		,		_snake_case					:    Union[str, Any]		)       ->   List[str]:


       """simple docstring"""






       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase_							:    Dict							=     super()._flatten_past_key_values_(_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       else:
        lowerCamelCase_							:    List[str]							=     super(_snake_case		,		self		)._flatten_past_key_values_(
            _snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)








from dataclasses import dataclass

UpperCamelCase											=      '''0.1'''
UpperCamelCase											=      '''Lucia Harcekova'''




@dataclass
class 	lowerCamelCase__				:
      lowerCamelCase_							:   int
      lowerCamelCase_							:   int
      lowerCamelCase_							:   str






      def __repr__(self					:    Tuple		)       ->   str:


       """simple docstring"""






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




class 	lowerCamelCase__				:






      def __init__(self					:    Optional[int]		,		_snake_case					:    int = 13		,		_snake_case					:    int = 6		)       ->   None:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		)       ->   list[Token]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     []
       lowerCamelCase_							:    List[str]							=     ''

       # 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)
        lowerCamelCase_							:    List[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:
         lowerCamelCase_							:    int							=     search_buffer[-self.search_buffer_size :]

        # update the text
        lowerCamelCase_							:    str							=     text[token.length + 1 :]

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

       return output






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    list[Token]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     ''

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

       return output






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    str		,		_snake_case					:    str		)       ->   Token:


       """simple docstring"""






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

       # Initialise result parameters to default values
       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     0, 0

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

       for i, character in enumerate(_snake_case		):
        lowerCamelCase_							:    Union[str, Any]							=     len(_snake_case		) - i
        if character == text[0]:
         lowerCamelCase_							:    str							=     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:
          lowerCamelCase_		,      lowerCamelCase_							:    str							=     found_offset, found_length

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






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   int:


       """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=1_3, 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."
