




import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """conditional_detr"""
   _a							=      ["""past_key_values"""]
   _a							=      {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }







   def __init__(	self      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							UpperCamelCase=3      ,							UpperCamelCase=300      ,							UpperCamelCase=6      ,							UpperCamelCase=2048      ,							UpperCamelCase=8      ,							UpperCamelCase=6      ,							UpperCamelCase=2048      ,							UpperCamelCase=8      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.0      ,							UpperCamelCase=True      ,							UpperCamelCase="relu"      ,							UpperCamelCase=256      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.02      ,							UpperCamelCase=1.0      ,							UpperCamelCase=False      ,							UpperCamelCase="sine"      ,							UpperCamelCase="resnet50"      ,							UpperCamelCase=True      ,							UpperCamelCase=False      ,							UpperCamelCase=2      ,							UpperCamelCase=5      ,							UpperCamelCase=2      ,							UpperCamelCase=1      ,							UpperCamelCase=1      ,							UpperCamelCase=2      ,							UpperCamelCase=5      ,							UpperCamelCase=2      ,							UpperCamelCase=0.25      ,							**UpperCamelCase      ,							)				->    int:
     if backbone_config is not None and use_timm_backbone:
       raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'      )

     if not use_timm_backbone:
       if backbone_config is None:
         logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'      )
         __a			=					CONFIG_MAPPING['resnet'](out_features=['stage4']      )
       elif isinstance(UpperCamelCase      ,							UpperCamelCase      ):
         __a			=					backbone_config.get('model_type'      )
         __a			=					CONFIG_MAPPING[backbone_model_type]
         __a			=					config_class.from_dict(UpperCamelCase      )

     __a			=					use_timm_backbone
     __a			=					backbone_config
     __a			=					num_channels
     __a			=					num_queries
     __a			=					d_model
     __a			=					encoder_ffn_dim
     __a			=					encoder_layers
     __a			=					encoder_attention_heads
     __a			=					decoder_ffn_dim
     __a			=					decoder_layers
     __a			=					decoder_attention_heads
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation_dropout
     __a			=					activation_function
     __a			=					init_std
     __a			=					init_xavier_std
     __a			=					encoder_layerdrop
     __a			=					decoder_layerdrop
     __a			=					encoder_layers
     __a			=					auxiliary_loss
     __a			=					position_embedding_type
     __a			=					backbone
     __a			=					use_pretrained_backbone
     __a			=					dilation
     # Hungarian matcher
     __a			=					class_cost
     __a			=					bbox_cost
     __a			=					giou_cost
     # Loss coefficients
     __a			=					mask_loss_coefficient
     __a			=					dice_loss_coefficient
     __a			=					cls_loss_coefficient
     __a			=					bbox_loss_coefficient
     __a			=					giou_loss_coefficient
     __a			=					focal_alpha
     super().__init__(is_encoder_decoder=UpperCamelCase      ,							**UpperCamelCase      )







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return self.encoder_attention_heads







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return self.d_model







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					copy.deepcopy(self.__dict__      )
     if self.backbone_config is not None:
       __a			=					self.backbone_config.to_dict()
     __a			=					self.__class__.model_type
     return output





class    __lowercase						(  __magic_name__ ):
   _a							=      version.parse("""1.11""" )







   @property
   def 		UpperCamelCase__			(	self      )				->    Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ('pixel_mask', {0: 'batch'}),
         ]      )







   @property
   def 		UpperCamelCase__			(	self      )				->    float:
     return 1e-5







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return 12









'''simple docstring'''


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							int				,					a_	:							Tuple				,					a_	:							Tuple			):
  __a			=					{
      'en': 'Machine learning is great, isn\'t it?',
      'ru': 'Машинное обучение - это здорово, не так ли?',
      'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  __a			=					{
      'wmt16-en-de-dist-12-1': [28.3, 27.52],
      'wmt16-en-de-dist-6-1': [27.4, 27.11],
      'wmt16-en-de-12-1': [26.9, 25.75],
  }
  __a			=					f"{src_lang}-{tgt_lang}"

  __a			=					f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
  model_card_dir.mkdir(parents=a_				,					exist_ok=a_			)
  __a			=					os.path.join(a_				,					'README.md'			)
  print(f"Generating {path}"			)
  with open(a_				,					'w'				,					encoding='utf-8'			) as f:
    f.write(a_			)


# make sure we are under the root of the project
UpperCAmelCase_ 		=      Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_ 		=      repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
  UpperCAmelCase_ 		=      model_cards_dir / "allenai" / model_name
  write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



