"""simple docstring"""


# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[int]:
     snake_case_       =      {
         '''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]
     snake_case_       =      {
         '''wmt19-de-en-6-6-base''': [0, 38.37],
         '''wmt19-de-en-6-6-big''': [0, 39.90],
     }
     snake_case_       =      f'{src_lang}-{tgt_lang}'

     snake_case_       =      f'\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt19 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\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\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   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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 wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -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/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\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=snake_case_		,      exist_ok=snake_case_      )
     snake_case_       =      os.path.join(snake_case_		,      'README.md'      )
     print(f'Generating {path}'      )
     with open(snake_case_		,      'w'		,      encoding='utf-8'      ) as f:
          f.write(snake_case_      )


# 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 ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
      __UpperCamelCase								=    model_cards_dir / "allenai" / model_name
      write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)


"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__=16,							lowerCAmelCase__=4,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   int:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      image_size // patch_size
            snake_case_       =      mask_input_channels
            snake_case_       =      num_point_embeddings
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__="relu",							lowerCAmelCase__=2048,							lowerCAmelCase__=2,							lowerCAmelCase__=8,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=256,							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      hidden_act
            snake_case_       =      mlp_dim
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      attention_downsample_rate
            snake_case_       =      num_multimask_outputs
            snake_case_       =      iou_head_depth
            snake_case_       =      iou_head_hidden_dim
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=768,							lowerCAmelCase__=256,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-06,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							lowerCAmelCase__=4.0,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=14,							lowerCAmelCase__=[2, 5, 8, 11],							lowerCAmelCase__=128,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)

            snake_case_       =      hidden_size
            snake_case_       =      output_channels
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      num_channels
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps
            snake_case_       =      attention_dropout
            snake_case_       =      initializer_range
            snake_case_       =      qkv_bias
            snake_case_       =      mlp_ratio
            snake_case_       =      use_abs_pos
            snake_case_       =      use_rel_pos
            snake_case_       =      window_size
            snake_case_       =      global_attn_indexes
            snake_case_       =      num_pos_feats
            snake_case_       =      int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "sam"
       SCREAMING_SNAKE_CASE_				=   True







       def __init__(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      vision_config if vision_config is not None else {}
            snake_case_       =      prompt_encoder_config if prompt_encoder_config is not None else {}
            snake_case_       =      mask_decoder_config if mask_decoder_config is not None else {}

            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      vision_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      prompt_encoder_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      mask_decoder_config.to_dict()

            snake_case_       =      SamVisionConfig(**lowerCAmelCase__)
            snake_case_       =      SamPromptEncoderConfig(**lowerCAmelCase__)
            snake_case_       =      SamMaskDecoderConfig(**lowerCAmelCase__)
            snake_case_       =      initializer_range







       def 	a_		(				self)   ->   Dict:
            snake_case_       =      copy.deepcopy(self.__dict__)
            snake_case_       =      self.vision_config.to_dict()
            snake_case_       =      self.prompt_encoder_config.to_dict()
            snake_case_       =      self.mask_decoder_config.to_dict()
            snake_case_       =      self.__class__.model_type
            return output

