
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCamelCase_						     =					"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowerCamelCase_						     =					"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

lowerCamelCase_						     =					"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,				_KWARGS_DESCRIPTION			)
class      __A(    datasets.Metric			):

		"""simple docstring"""







		def 	UpperCAmelCase_ (self						):
							if version.parse(scb.__version__						) < version.parse("""1.4.12"""						):
												raise ImportWarning(
												    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"""
												    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""						)
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,      citation=_CITATION	,      homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""	,      inputs_description=_KWARGS_DESCRIPTION	,      features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""	,      id="""sequence"""						),
							            """references""": datasets.Sequence(datasets.Value("""string"""	,      id="""sequence"""						)	,      id="""references"""						),
							        }						)	,      codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]	,      reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ]	,      )







		def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = CHRF.CHAR_ORDER	,      SCREAMING_SNAKE_CASE_ = CHRF.WORD_ORDER	,      SCREAMING_SNAKE_CASE_ = CHRF.BETA	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      ):
							UpperCamelCase__				=    len(references[0]						)
							if any(len(A__						) != references_per_prediction for refs in references						):
												raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
							UpperCamelCase__				=    [[refs[i] for refs in references] for i in range(A__						)]

							UpperCamelCase__				=    CHRF(A__	,      A__	,      A__	,      A__	,      A__	,      A__						)
							UpperCamelCase__				=    sb_chrf.corpus_score(A__	,      A__						)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Tuple	,		__a      :	int	,		__a      :	Dict="attention"						):



     '''simple docstring'''




     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"]
     return k, o, q, v






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Any	,		__a      :	Dict	,		__a      :	Optional[Any]=False						):



     '''simple docstring'''




     if split_mlp_wi:
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"]
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"]
          UpperCamelCase__				=    (wi_a, wi_a)
     else:
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi/kernel"]

     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wo/kernel"]
     return wi, wo






def 						__magic_name__			(   __a      :	Tuple	,		__a      :	Tuple	,		__a      :	Optional[int]	,		__a      :	Dict						):



     '''simple docstring'''




     return params[f"{prefix}/layers_{i}/{layer_name}/scale"]






def 						__magic_name__			(   __a      :	dict	,		*, __a      :	int	,		__a      :	bool						):



     '''simple docstring'''




     UpperCamelCase__				=    traverse_util.flatten_dict(variables["""target"""]						)
     UpperCamelCase__				=    {"""/""".join(__a						): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     UpperCamelCase__				=    """encoder/layers_0/mlp/wi_0/kernel""" in old
     print("""Split MLP:"""	,		__a						)

     UpperCamelCase__				=    collections.OrderedDict()

     # Shared embeddings.
     UpperCamelCase__				=    old["""token_embedder/embedding"""]

     # Encoder.
     for i in range(__a						):
          # Block i, layer 0 (Self Attention).
          UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""encoder"""	,		"""pre_attention_layer_norm"""						)
          UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""encoder"""	,		"""attention"""						)
          UpperCamelCase__				=    layer_norm
          UpperCamelCase__				=    k.T
          UpperCamelCase__				=    o.T
          UpperCamelCase__				=    q.T
          UpperCamelCase__				=    v.T

          # Block i, layer 1 (MLP).
          UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""encoder"""	,		"""pre_mlp_layer_norm"""						)
          UpperCamelCase__		,	UpperCamelCase__				=    tax_mlp_lookup(__a	,		__a	,		"""encoder"""	,		__a						)
          UpperCamelCase__				=    layer_norm
          if split_mlp_wi:
               UpperCamelCase__				=    wi[0].T
               UpperCamelCase__				=    wi[1].T
          else:
               UpperCamelCase__				=    wi.T
          UpperCamelCase__				=    wo.T

     UpperCamelCase__				=    old[
         """encoder/relpos_bias/rel_embedding"""
     ].T
     UpperCamelCase__				=    old["""encoder/encoder_norm/scale"""]

     if not is_encoder_only:
          # Decoder.
          for i in range(__a						):
               # Block i, layer 0 (Self Attention).
               UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""decoder"""	,		"""pre_self_attention_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""decoder"""	,		"""self_attention"""						)
               UpperCamelCase__				=    layer_norm
               UpperCamelCase__				=    k.T
               UpperCamelCase__				=    o.T
               UpperCamelCase__				=    q.T
               UpperCamelCase__				=    v.T

               # Block i, layer 1 (Cross Attention).
               UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""decoder"""	,		"""pre_cross_attention_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    tax_attention_lookup(__a	,		__a	,		"""decoder"""	,		"""encoder_decoder_attention"""						)
               UpperCamelCase__				=    layer_norm
               UpperCamelCase__				=    k.T
               UpperCamelCase__				=    o.T
               UpperCamelCase__				=    q.T
               UpperCamelCase__				=    v.T

               # Block i, layer 2 (MLP).
               UpperCamelCase__				=    tax_layer_norm_lookup(__a	,		__a	,		"""decoder"""	,		"""pre_mlp_layer_norm"""						)
               UpperCamelCase__		,	UpperCamelCase__				=    tax_mlp_lookup(__a	,		__a	,		"""decoder"""	,		__a						)
               UpperCamelCase__				=    layer_norm
               if split_mlp_wi:
                    UpperCamelCase__				=    wi[0].T
                    UpperCamelCase__				=    wi[1].T
               else:
                    UpperCamelCase__				=    wi.T
               UpperCamelCase__				=    wo.T

          UpperCamelCase__				=    old["""decoder/decoder_norm/scale"""]
          UpperCamelCase__				=    old[
              """decoder/relpos_bias/rel_embedding"""
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               UpperCamelCase__				=    old["""decoder/logits_dense/kernel"""].T

     return new






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	bool						):



     '''simple docstring'''




     UpperCamelCase__				=    collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          UpperCamelCase__				=    state_dict["""shared.weight"""]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               UpperCamelCase__				=    state_dict["""shared.weight"""]

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print("""Using shared word embeddings as lm_head."""						)
               UpperCamelCase__				=    state_dict["""shared.weight"""]

     return state_dict






def 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	int	,		__a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__				=    checkpoints.load_tax_checkpoint(__a						)
     UpperCamelCase__				=    convert_tax_to_pytorch(__a	,		num_layers=config.num_layers	,		is_encoder_only=__a						)
     UpperCamelCase__				=    make_state_dict(__a	,		__a						)
     model.load_state_dict(__a	,		strict=__a						)






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Optional[Any]	,		__a      :	Any	,		__a      :	bool = False						):



     '''simple docstring'''




     UpperCamelCase__				=    TaConfig.from_json_file(__a						)
     print(f"Building PyTorch model from configuration: {config}"						)
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          UpperCamelCase__				=    TaEncoderModel(__a						)
     else:
          UpperCamelCase__				=    TaForConditionalGeneration(__a						)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(__a	,		__a	,		__a	,		__a						)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"						)
     model.save_pretrained(__a						)

     # Verify that we can load the checkpoint.
     model.from_pretrained(__a						)
     print("""Done"""						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





