"""simple docstring"""





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  :		Optional[int]           =       logging.get_logger(__name__)

_UpperCamelCase  :		int           =       {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}


class 	UpperCAmelCase_      (       _a):
      lowerCamelCase__						:       Dict					    =    "blenderbot-small"
      lowerCamelCase__						:       Optional[int]					    =    ["past_key_values"]
      lowerCamelCase__						:       Union[str, Any]					    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


      def __init__(  self						,     a=5_0_2_6_5						,     a=5_1_2						,     a=8						,     a=2_0_4_8						,     a=1_6						,     a=8						,     a=2_0_4_8						,     a=1_6						,     a=0.0						,     a=0.0						,     a=True						,     a=True						,     a="gelu"						,     a=5_1_2						,     a=0.1						,     a=0.0						,     a=0.0						,     a=0.02						,     a=1						,     a=False						,     a=0						,     a=1						,     a=2						,     a=2						,     **a						,     )			->   List[Any]:
             lowercase__     :						str         =      vocab_size
             lowercase__     :						str         =      max_position_embeddings
             lowercase__     :						Any         =      d_model
             lowercase__     :						int         =      encoder_ffn_dim
             lowercase__     :						List[str]         =      encoder_layers
             lowercase__     :						Dict         =      encoder_attention_heads
             lowercase__     :						List[str]         =      decoder_ffn_dim
             lowercase__     :						Tuple         =      decoder_layers
             lowercase__     :						Dict         =      decoder_attention_heads
             lowercase__     :						List[Any]         =      dropout
             lowercase__     :						Union[str, Any]         =      attention_dropout
             lowercase__     :						Tuple         =      activation_dropout
             lowercase__     :						str         =      activation_function
             lowercase__     :						Any         =      init_std
             lowercase__     :						str         =      encoder_layerdrop
             lowercase__     :						int         =      decoder_layerdrop
             lowercase__     :						Tuple         =      use_cache
             lowercase__     :						Dict         =      encoder_layers
             lowercase__     :						Optional[Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 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_						,     )





class 	UpperCAmelCase_      (       _a):


      @property
      def 						_UpperCAmelCase			(  self				)			->   Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__     :						List[Any]         =      OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]				)

                    if self.use_past:
                           lowercase__     :						Tuple         =      {0: 'batch'}
                           lowercase__     :						Optional[int]         =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           lowercase__     :						List[Any]         =      {0: 'batch', 1: 'decoder_sequence'}
                           lowercase__     :						List[Any]         =      {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.
                    lowercase__     :						str         =      OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]				)
                    if self.use_past:
                           lowercase__       , lowercase__     :						Optional[int]         =      self.num_layers
                           for i in range(UpperCamelCase_				):
                                  lowercase__     :						Optional[int]         =      {0: 'batch', 2: 'past_sequence + sequence'}
                                  lowercase__     :						List[Any]         =      {0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    lowercase__     :						str         =      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				)			->   Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__     :						int         =      super().outputs
             else:
                    lowercase__     :						str         =      super(UpperCamelCase_						,     self				).outputs
                    if self.use_past:
                           lowercase__       , lowercase__     :						Any         =      self.num_layers
                           for i in range(UpperCamelCase_				):
                                  lowercase__     :						Union[str, Any]         =      {0: 'batch', 2: 'past_sequence + sequence'}
                                  lowercase__     :						Union[str, Any]         =      {0: 'batch', 2: 'past_sequence + sequence'}
             return common_outputs


      def 						_UpperCAmelCase			(  self						,     a						,     a = -1						,     a = -1						,     a = False						,     a = None						,     )			->   Mapping[str, Any]:
             lowercase__     :						Optional[Any]         =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_				)

             # Generate decoder inputs
             lowercase__     :						int         =      seq_length if not self.use_past else 1
             lowercase__     :						str         =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_				)
             lowercase__     :						Optional[int]         =      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             lowercase__     :						Optional[Any]         =      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
                    lowercase__       , lowercase__     :						List[Any]         =      common_inputs['input_ids'].shape
                    lowercase__     :						Union[str, Any]         =      common_inputs['decoder_input_ids'].shape[1]
                    lowercase__       , lowercase__     :						Union[str, Any]         =      self.num_attention_heads
                    lowercase__     :						int         =      (
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    lowercase__     :						List[Any]         =      decoder_seq_length + 3
                    lowercase__     :						Optional[Any]         =      (
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )

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

                    lowercase__     :						str         =      []
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    lowercase__       , lowercase__     :						Dict         =      self.num_layers
                    lowercase__     :						List[Any]         =      min(UpperCamelCase_						,     UpperCamelCase_				)
                    lowercase__     :						Tuple         =      max(UpperCamelCase_						,     UpperCamelCase_				) - min_num_layers
                    lowercase__     :						List[str]         =      '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.
                    lowercase__     :						str         =      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 						_UpperCAmelCase			(  self						,     a						,     a = -1						,     a = -1						,     a = False						,     a = None						,     )			->   Mapping[str, Any]:
             lowercase__     :						int         =      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
                    lowercase__       , lowercase__     :						List[Any]         =      common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    lowercase__     :						List[Any]         =      seqlen + 2
                    lowercase__       , lowercase__     :						Any         =      self.num_layers
                    lowercase__       , lowercase__     :						int         =      self.num_attention_heads
                    lowercase__     :						Optional[int]         =      (
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    lowercase__     :						Dict         =      common_inputs['attention_mask'].dtype
                    lowercase__     :						Tuple         =      torch.cat(
                        [common_inputs['attention_mask'], torch.ones(UpperCamelCase_						,     UpperCamelCase_						,     dtype=UpperCamelCase_				)]						,     dim=1				)
                    lowercase__     :						List[Any]         =      [
                        (torch.zeros(UpperCamelCase_				), torch.zeros(UpperCamelCase_				)) for _ in range(UpperCamelCase_				)
                    ]
             return common_inputs


      def 						_UpperCAmelCase			(  self						,     a						,     a = -1						,     a = -1						,     a = False						,     a = None						,     )			->   Mapping[str, Any]:
             # 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
             lowercase__     :						Union[str, Any]         =      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
             lowercase__     :						List[Any]         =      tokenizer.num_special_tokens_to_add(UpperCamelCase_				)
             lowercase__     :						Union[str, Any]         =      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
             lowercase__     :						List[str]         =      [' '.join([tokenizer.unk_token]				) * seq_length] * batch_size
             lowercase__     :						int         =      dict(tokenizer(UpperCamelCase_						,     return_tensors=UpperCamelCase_				)				)
             return common_inputs


      def 						_UpperCAmelCase			(  self						,     a						,     a = -1						,     a = -1						,     a = False						,     a = None						,     )			->   Mapping[str, Any]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__     :						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":
                    lowercase__     :						int         =      self._generate_dummy_inputs_for_causal_lm(
                        UpperCamelCase_						,     batch_size=UpperCamelCase_						,     seq_length=UpperCamelCase_						,     is_pair=UpperCamelCase_						,     framework=UpperCamelCase_				)
             else:
                    lowercase__     :						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 						_UpperCAmelCase			(  self						,     a						,     a						,     a						,     a				)			->   List[str]:
             if self.task in ["default", "seq2seq-lm"]:
                    lowercase__     :						Any         =      super()._flatten_past_key_values_(UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_				)
             else:
                    lowercase__     :						Any         =      super(UpperCamelCase_						,     self				)._flatten_past_key_values_(
                        UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_				)








'''simple docstring'''




import enum
import shutil
import sys


a__      ,       a__					:			Any          =     shutil.get_terminal_size()

a__					:			Optional[int]          =     {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}






class 				__snake_case     (					enum.Enum							):
	__lowerCAmelCase       						=  0
	__lowerCAmelCase       						=  1





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->Optional[Any]:
	sys.stdout.write(str(UpperCAmelCase_							) + end							)
	sys.stdout.flush()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->List[str]:
	forceWrite(f'''\u001b[{color}m{content}\u001b[0m'''					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->Optional[Any]:
	forceWrite('\r'							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	forceWrite(f'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''							)




def 						__lowerCamelCase			(	)   ->str:
	forceWrite(' ' * TERMINAL_WIDTH							)
	reset_cursor()







def 						__lowerCamelCase			(	)   ->Tuple:
	reset_cursor()
	forceWrite('-' * TERMINAL_WIDTH							)



