





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

import os
from pathlib import Path




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						str				)  ->							int:
    UpperCAmelCase_										=					{
        '''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]
    UpperCAmelCase_										=					{
        '''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],
    }
    UpperCAmelCase_										=					f'{src_lang}-{tgt_lang}'

    UpperCAmelCase_										=					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=__UpperCamelCase	,					exist_ok=__UpperCamelCase				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''README.md'''				)
    print(f'Generating {path}'				)
    with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''				) as f:
        f.write(__UpperCamelCase				)


# make sure we are under the root of the project
_lowerCamelCase      			=						Path(__file__).resolve().parent.parent.parent
_lowerCamelCase      			=						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"]:
 _lowerCamelCase      			=						model_cards_dir / 'allenai' / model_name
 write_model_card(model_card_dir, src_lang='en', tgt_lang='de', model_name=model_name)











import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

    UpperCAmelCase_										=					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        UpperCAmelCase_										=					'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        UpperCAmelCase_										=					'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''				)

    # Encoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''encoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_global_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo
        UpperCAmelCase_										=					tax_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_encoder_layer_block

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        UpperCAmelCase_										=					tax_encoder_global_rel_embedding

    # Assigning
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    UpperCAmelCase_										=					tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''decoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_pre_attention_layer_norm

        UpperCAmelCase_										=					tax_enc_dec_attention_key
        UpperCAmelCase_										=					tax_enc_dec_attention_out
        UpperCAmelCase_										=					tax_enc_dec_attention_query
        UpperCAmelCase_										=					tax_enc_dec_attention_value

        UpperCAmelCase_										=					tax_cross_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo

        UpperCAmelCase_										=					txa_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_decoder_layer_block

    # Decoder Normalization
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    UpperCAmelCase_										=					txa_decoder_norm

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_decoder_rel_embedding

    # Token Embeddings
    UpperCAmelCase_										=					tax_model['''target''']['''token_embedder''']['''embedding''']
    UpperCAmelCase_										=					txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(__UpperCamelCase				)
    print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





