

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def       UpperCAmelCase__    (      _A				:			Union[str, Any]     ,				_A				:			Any     ,				_A				:			Any							):





  '''simple docstring'''







  a__  =TaConfig.from_pretrained(lowercase__							)
  a__  =FlaxTaForConditionalGeneration(config=lowercase__							)
  a__  =checkpoints.load_tax_checkpoint(lowercase__							)

  a__  ="wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  # Encoder
  for layer_index in range(config.num_layers							):
    a__  =F"""layers_{str(lowercase__							)}"""

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

    # Layer Normalization
    a__  =tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

    a__  =tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    a__  =tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    a__  =tax_attention_key
    a__  =tax_attention_out
    a__  =tax_attention_query
    a__  =tax_attention_value

    a__  =tax_attention_layer_norm

    if split_mlp_wi:
      a__  =tax_mlp_wi_a
      a__  =tax_mlp_wi_a
    else:
      a__  =tax_mlp_wi

    a__  =tax_mlp_wo
    a__  =tax_mlp_layer_norm

  # Only for layer 0:
  a__  =tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
  a__  =tax_encoder_rel_embedding

  # Assigning
  a__  =tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  a__  =tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers							):
    a__  =F"""layers_{str(lowercase__							)}"""

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

    # Layer Normalization
    a__  =tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    a__  =tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
        "kernel"
    ]
    a__  =tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
        "kernel"
    ]
    a__  =tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
        "kernel"
    ]
    a__  =tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
        "kernel"
    ]

    # Layer Normalization
    a__  =tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

    a__  =tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    a__  =tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    a__  =tax_attention_key
    a__  =tax_attention_out
    a__  =tax_attention_query
    a__  =tax_attention_value

    a__  =tax_pre_attention_layer_norm

    a__  =tax_enc_dec_attention_key
    a__  =tax_enc_dec_attention_out
    a__  =tax_enc_dec_attention_query
    a__  =tax_enc_dec_attention_value

    a__  =tax_cross_layer_norm

    if split_mlp_wi:
      a__  =tax_mlp_wi_a
      a__  =tax_mlp_wi_a
    else:
      a__  =tax_mlp_wi

    a__  =tax_mlp_wo

    a__  =txa_mlp_layer_norm

  # Decoder Normalization
  a__  =tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  a__  =txa_decoder_norm

  # Only for layer 0:
  a__  =tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  a__  =tax_decoder_rel_embedding

  # Token Embeddings
  a__  =tax_model["target"]["token_embedder"]["embedding"]
  a__  =txa_token_embeddings

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

  flax_model.save_pretrained(lowercase__							)
  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 TX5 checkpoint.'''
       )
       parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of 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)


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  a__  =min(re.search('''^ +'''     ,				_A							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
  return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A							)]							)


lowerCamelCase									= '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCamelCase									= '''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCamelCase									= '''\
# Begin of markdown

Some cool dataset card
'''







class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =Path(lowercase_	) / '''README.md'''
       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertEqual(lowercase_,   {}	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
     with self.assertRaises(lowercase_	):
       DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)
