





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

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


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Tuple       = '''blenderbot'''
       _UpperCamelCase					:       Optional[Any]       = ['''past_key_values''']
       _UpperCamelCase					:       Optional[int]       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]=8008       , _SCREAMING_SNAKE_CASE:			Any=128       , _SCREAMING_SNAKE_CASE:			int=2       , _SCREAMING_SNAKE_CASE:			str=10240       , _SCREAMING_SNAKE_CASE:			List[Any]=32       , _SCREAMING_SNAKE_CASE:			List[Any]=24       , _SCREAMING_SNAKE_CASE:			Any=10240       , _SCREAMING_SNAKE_CASE:			List[str]=32       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=0.0       , _SCREAMING_SNAKE_CASE:			Dict=0.0       , _SCREAMING_SNAKE_CASE:			Dict=True       , _SCREAMING_SNAKE_CASE:			Tuple=True       , _SCREAMING_SNAKE_CASE:			Tuple="gelu"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2560       , _SCREAMING_SNAKE_CASE:			Dict=0.1       , _SCREAMING_SNAKE_CASE:			List[str]=0.0       , _SCREAMING_SNAKE_CASE:			Any=0.0       , _SCREAMING_SNAKE_CASE:			str=0.02       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			List[Any]=False       , _SCREAMING_SNAKE_CASE:			Tuple=0       , _SCREAMING_SNAKE_CASE:			Tuple=1       , _SCREAMING_SNAKE_CASE:			Dict=2       , _SCREAMING_SNAKE_CASE:			List[str]=3       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , **_SCREAMING_SNAKE_CASE:			str       , )							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	d_model
              UpperCamelCase_ 							=	encoder_ffn_dim
              UpperCamelCase_ 							=	encoder_layers
              UpperCamelCase_ 							=	encoder_attention_heads
              UpperCamelCase_ 							=	decoder_ffn_dim
              UpperCamelCase_ 							=	decoder_layers
              UpperCamelCase_ 							=	decoder_attention_heads
              UpperCamelCase_ 							=	dropout
              UpperCamelCase_ 							=	attention_dropout
              UpperCamelCase_ 							=	activation_dropout
              UpperCamelCase_ 							=	activation_function
              UpperCamelCase_ 							=	init_std
              UpperCamelCase_ 							=	encoder_layerdrop
              UpperCamelCase_ 							=	decoder_layerdrop
              UpperCamelCase_ 							=	use_cache
              UpperCamelCase_ 							=	encoder_layers
              UpperCamelCase_ 							=	scale_embedding  # scale factor will be sqrt(d_model) if True

              super().__init__(
                  pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , is_encoder_decoder=_SCREAMING_SNAKE_CASE       , decoder_start_token_id=_SCREAMING_SNAKE_CASE       , encoder_no_repeat_ngram_size=_SCREAMING_SNAKE_CASE       , forced_eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       , )



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @property
       def lowercase			(		self:			Dict		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase_ 							=	OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]		)
                     if self.use_past:
                            UpperCamelCase_ 							=	{0: "batch"}
                            UpperCamelCase_ 							=	{0: "batch", 1: "past_decoder_sequence + sequence"}
                     else:
                            UpperCamelCase_ 							=	{0: "batch", 1: "decoder_sequence"}
                            UpperCamelCase_ 							=	{0: "batch", 1: "decoder_sequence"}
                     if self.use_past:
                            self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       , direction="inputs"		)
              elif self.task == "causal-lm":
                     UpperCamelCase_ 							=	OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]		)
                     if self.use_past:
                            UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_layers
                            for i in range(_SCREAMING_SNAKE_CASE		):
                                   UpperCamelCase_ 							=	{0: "batch", 2: "past_sequence + sequence"}
                                   UpperCamelCase_ 							=	{0: "batch", 2: "past_sequence + sequence"}
              else:
                     UpperCamelCase_ 							=	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 lowercase			(		self:			str		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


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

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			PreTrainedTokenizer       , _SCREAMING_SNAKE_CASE:			int = -1       , _SCREAMING_SNAKE_CASE:			int = -1       , _SCREAMING_SNAKE_CASE:			bool = False       , _SCREAMING_SNAKE_CASE:			Optional[TensorType] = None       , )							->			Mapping[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              # Generate decoder inputs
              UpperCamelCase_ 							=	seq_length if not self.use_past else 1
              UpperCamelCase_ 							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
              UpperCamelCase_ 							=	dict(**_SCREAMING_SNAKE_CASE       , **_SCREAMING_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
                     UpperCamelCase_							,			UpperCamelCase_ 							=	common_inputs["input_ids"].shape
                     UpperCamelCase_ 							=	common_inputs["decoder_input_ids"].shape[1]
                     UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_attention_heads
                     UpperCamelCase_ 							=	(
                         batch,
                         num_encoder_attention_heads,
                         encoder_seq_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     UpperCamelCase_ 							=	decoder_seq_length
                     UpperCamelCase_ 							=	(
                         batch,
                         num_decoder_attention_heads,
                         decoder_past_length,
                         self._config.hidden_size // num_decoder_attention_heads,
                     )
                     UpperCamelCase_ 							=	torch.cat(
                         [common_inputs["decoder_attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)]       , dim=1		)
                     UpperCamelCase_ 							=	[]
                     UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_layers

                     for _ in range(_SCREAMING_SNAKE_CASE		):
                            common_inputs["past_key_values"].append(
                                (
                                    torch.zeros(_SCREAMING_SNAKE_CASE		),
                                    torch.zeros(_SCREAMING_SNAKE_CASE		),
                                    torch.zeros(_SCREAMING_SNAKE_CASE		),
                                    torch.zeros(_SCREAMING_SNAKE_CASE		),
                                )		)
              return common_inputs

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




              """simple docstring"""


              UpperCamelCase_ 							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_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
                     UpperCamelCase_							,			UpperCamelCase_ 							=	common_inputs["input_ids"].shape
                     UpperCamelCase_ 							=	seqlen
                     UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_layers
                     UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_attention_heads
                     UpperCamelCase_ 							=	(
                         batch,
                         num_encoder_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     UpperCamelCase_ 							=	common_inputs["attention_mask"].dtype
                     UpperCamelCase_ 							=	torch.cat(
                         [common_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)]       , dim=1		)
                     UpperCamelCase_ 							=	[
                         (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
                     ]
              return common_inputs

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




              """simple docstring"""


              UpperCamelCase_ 							=	compute_effective_axis_dimension(
                  _SCREAMING_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
              UpperCamelCase_ 							=	tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	compute_effective_axis_dimension(
                  _SCREAMING_SNAKE_CASE       , fixed_dimension=OnnxConfig.default_fixed_sequence       , num_token_to_add=_SCREAMING_SNAKE_CASE		)

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

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




              """simple docstring"""


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

              elif self.task == "causal-lm":
                     UpperCamelCase_ 							=	self._generate_dummy_inputs_for_causal_lm(
                         _SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , seq_length=_SCREAMING_SNAKE_CASE       , is_pair=_SCREAMING_SNAKE_CASE       , framework=_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                         _SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , seq_length=_SCREAMING_SNAKE_CASE       , is_pair=_SCREAMING_SNAKE_CASE       , framework=_SCREAMING_SNAKE_CASE		)

              return common_inputs

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any		)							->			int:




              """simple docstring"""


              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase_ 							=	super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	super(_SCREAMING_SNAKE_CASE       , self		)._flatten_past_key_values_(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Mapping[str, Mapping[int, str]]       , _SCREAMING_SNAKE_CASE:			str		)							->			Optional[Any]:




              """simple docstring"""


              if direction not in ["inputs", "outputs"]:
                     raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''		)

              UpperCamelCase_ 							=	"past_key_values" if direction == "inputs" else "present"
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.num_layers

              UpperCamelCase_ 							=	"past_encoder_sequence"
              UpperCamelCase_ 							=	"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

              for i in range(_SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	{0: "batch", 2: decoder_sequence}
                     UpperCamelCase_ 							=	{0: "batch", 2: decoder_sequence}
                     UpperCamelCase_ 							=	{0: "batch", 2: encoder_sequence}
                     UpperCamelCase_ 							=	{0: "batch", 2: encoder_sequence}








def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	word.upper()
       UpperCamelCase_ 							=	0
       UpperCamelCase_ 							=	{}
       UpperCamelCase_ 							=	[]

       for letter in word:
              if letter not in letter_nums:
                     UpperCamelCase_ 							=	str(UpperCamelCase_	)
                     next_num += 1
              word_pattern.append(letter_nums[letter]	)
       return ".".join(UpperCamelCase_	)


if __name__ == "__main__":
     import pprint
     import time

     _UpperCAmelCase										=					time.time()
     with open('dictionary.txt') as in_file:
          _UpperCAmelCase										=					in_file.read().splitlines()

     _UpperCAmelCase										=					{}
     for word in word_list:
          _UpperCAmelCase										=					get_word_pattern(word)
          if pattern in all_patterns:
               all_patterns[pattern].append(word)
          else:
               _UpperCAmelCase										=					[word]

     with open('word_patterns.txt', 'w') as out_file:
          out_file.write(pprint.pformat(all_patterns))

     _UpperCAmelCase										=					round(time.time() - start_time, 2)
     print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
     # Done!  9,581 word patterns found in 0.58 seconds.

