'''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


lowerCAmelCase_ : List[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : List[Any]						  =      {
    "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 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					='blenderbot-small'
   lowerCamelCase_    					=['past_key_values']
   lowerCamelCase_    					={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


   def __init__(   self				:	List[str]   ,		__lowerCAmelCase				:	Union[str, Any]=5_0265   ,		__lowerCAmelCase				:	List[str]=512   ,		__lowerCAmelCase				:	Dict=8   ,		__lowerCAmelCase				:	Dict=2048   ,		__lowerCAmelCase				:	List[str]=16   ,		__lowerCAmelCase				:	Optional[int]=8   ,		__lowerCAmelCase				:	Tuple=2048   ,		__lowerCAmelCase				:	List[str]=16   ,		__lowerCAmelCase				:	str=0.0   ,		__lowerCAmelCase				:	Optional[int]=0.0   ,		__lowerCAmelCase				:	str=True   ,		__lowerCAmelCase				:	Tuple=True   ,		__lowerCAmelCase				:	Union[str, Any]="gelu"   ,		__lowerCAmelCase				:	Optional[int]=512   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	List[str]=0.0   ,		__lowerCAmelCase				:	Optional[Any]=0.0   ,		__lowerCAmelCase				:	Union[str, Any]=0.02   ,		__lowerCAmelCase				:	Union[str, Any]=1   ,		__lowerCAmelCase				:	List[str]=False   ,		__lowerCAmelCase				:	int=0   ,		__lowerCAmelCase				:	Optional[int]=1   ,		__lowerCAmelCase				:	List[Any]=2   ,		__lowerCAmelCase				:	List[str]=2   ,		**__lowerCAmelCase				:	int   ,		)      ->					List[Any]:
         lowercase_     =      vocab_size
         lowercase_     =      max_position_embeddings
         lowercase_     =      d_model
         lowercase_     =      encoder_ffn_dim
         lowercase_     =      encoder_layers
         lowercase_     =      encoder_attention_heads
         lowercase_     =      decoder_ffn_dim
         lowercase_     =      decoder_layers
         lowercase_     =      decoder_attention_heads
         lowercase_     =      dropout
         lowercase_     =      attention_dropout
         lowercase_     =      activation_dropout
         lowercase_     =      activation_function
         lowercase_     =      init_std
         lowercase_     =      encoder_layerdrop
         lowercase_     =      decoder_layerdrop
         lowercase_     =      use_cache
         lowercase_     =      encoder_layers
         lowercase_     =      scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 							lowercase						(						__lowerCamelCase ):


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

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


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	PreTrainedTokenizer   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[TensorType] = None   ,		)      ->					Mapping[str, Any]:
         lowercase_     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)

         # Generate decoder inputs
         lowercase_     =      seq_length if not self.use_past else 1
         lowercase_     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         lowercase_     =      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
               lowercase_ ,							lowercase_     =      common_inputs["input_ids"].shape
               lowercase_     =      common_inputs["decoder_input_ids"].shape[1]
               lowercase_ ,							lowercase_     =      self.num_attention_heads
               lowercase_     =      (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase_     =      decoder_seq_length + 3
               lowercase_     =      (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               lowercase_     =      torch.cat(
                   [common_inputs["decoder_attention_mask"], torch.ones(__lowerCAmelCase   ,		__lowerCAmelCase)]   ,		dim=1)

               lowercase_     =      []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               lowercase_ ,							lowercase_     =      self.num_layers
               lowercase_     =      min(__lowerCAmelCase   ,		__lowerCAmelCase)
               lowercase_     =      max(__lowerCAmelCase   ,		__lowerCAmelCase) - min_num_layers
               lowercase_     =      "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.
               lowercase_     =      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 __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	PreTrainedTokenizer   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[TensorType] = None   ,		)      ->					Mapping[str, Any]:
         lowercase_     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __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
               lowercase_ ,							lowercase_     =      common_inputs["input_ids"].shape
               # Not using the same length for past_key_values
               lowercase_     =      seqlen + 2
               lowercase_ ,							lowercase_     =      self.num_layers
               lowercase_ ,							lowercase_     =      self.num_attention_heads
               lowercase_     =      (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               lowercase_     =      common_inputs["attention_mask"].dtype
               lowercase_     =      torch.cat(
                   [common_inputs["attention_mask"], torch.ones(__lowerCAmelCase   ,		__lowerCAmelCase   ,		dtype=__lowerCAmelCase)]   ,		dim=1)
               lowercase_     =      [
                   (torch.zeros(__lowerCAmelCase), torch.zeros(__lowerCAmelCase)) for _ in range(__lowerCAmelCase)
               ]
         return common_inputs


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	PreTrainedTokenizer   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[TensorType] = 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_     =      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
         lowercase_     =      tokenizer.num_special_tokens_to_add(__lowerCAmelCase)
         lowercase_     =      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
         lowercase_     =      [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase_     =      dict(tokenizer(__lowerCAmelCase   ,		return_tensors=__lowerCAmelCase))
         return common_inputs


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	PreTrainedTokenizer   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	int = -1   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[TensorType] = None   ,		)      ->					Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   __lowerCAmelCase   ,		batch_size=__lowerCAmelCase   ,		seq_length=__lowerCAmelCase   ,		is_pair=__lowerCAmelCase   ,		framework=__lowerCAmelCase)

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

         return common_inputs


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Dict)      ->					Optional[Any]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     =      super()._flatten_past_key_values_(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         else:
               lowercase_     =      super(__lowerCAmelCase   ,		self)._flatten_past_key_values_(
                   __lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)







'''simple docstring'''

import json
import sys


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Optional[Any]:
      '''simple docstring'''


      with open(__lowerCamelCase ,		encoding="utf-8"				) as f:
            lowercase_     =      json.load(__lowerCamelCase				)

      lowercase_     =      ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

      for benchmark_name in sorted(__lowerCamelCase				):
            lowercase_     =      results[benchmark_name]

            lowercase_     =      benchmark_name.split("/"				)[-1]
            output_md.append(f'### Benchmark: {benchmark_file_name}'				)

            lowercase_     =      "| metric |"
            lowercase_     =      "|--------|"
            lowercase_     =      "| new / old (diff) |"
            for metric_name in sorted(__lowerCamelCase				):
                  lowercase_     =      benchmark_res[metric_name]
                  lowercase_     =      metric_vals["new"]
                  lowercase_     =      metric_vals.get("old" ,		__lowerCamelCase				)
                  lowercase_     =      metric_vals.get("diff" ,		__lowerCamelCase				)

                  lowercase_     =      f' {new_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  if old_val is not None:
                        val_str += f' / {old_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"
                  if dif_val is not None:
                        val_str += f' ({dif_val:f})' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  title += " " + metric_name + " |"
                  lines += "---|"
                  value += val_str + " |"

            output_md += [title, lines, value, " "]

      output_md.append("</details>"				)

      with open(__lowerCamelCase ,		"w" ,		encoding="utf-8"				) as f:
            f.writelines("\n".join(__lowerCamelCase				)				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[Any]						  =      sys.argv[1]
       lowerCAmelCase_ : Any						  =      sys.argv[2]

       format_json_to_md(input_json_file, output_md_file)






