




'''simple docstring'''


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_snake_case   :   Union[str, Any]     							=	{
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       list(s_dict.keys()   )
							for key in keys:
														_a			  =       R'''.*/layers_(\d+)'''
														_a			  =       key
														if re.match(lowerCAmelCase__				,		lowerCAmelCase__   ):
																					_a			  =       re.sub(R'''layers_(\d+)'''				,		R'''block/\1/layer'''				,		lowerCAmelCase__   )

														_a			  =       R'''(encoder|decoder)\/'''

														if re.match(lowerCAmelCase__				,		lowerCAmelCase__   ):
																					_a			  =       re.match(lowerCAmelCase__				,		lowerCAmelCase__   ).groups()
																					if groups[0] == "encoder":
																												_a			  =       re.sub(R'''/mlp/'''				,		R'''/1/mlp/'''				,		lowerCAmelCase__   )
																												_a			  =       re.sub(R'''/pre_mlp_layer_norm/'''				,		R'''/1/layer_norm/'''				,		lowerCAmelCase__   )

																					elif groups[0] == "decoder":
																												_a			  =       re.sub(R'''/mlp/'''				,		R'''/2/mlp/'''				,		lowerCAmelCase__   )
																												_a			  =       re.sub(R'''/pre_mlp_layer_norm/'''				,		R'''/2/layer_norm/'''				,		lowerCAmelCase__   )

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												_a			  =       new_key.replace(lowerCAmelCase__				,		lowerCAmelCase__   )

														print(f'{key} -> {new_key}'   )
														_a			  =       s_dict.pop(lowerCAmelCase__   )

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														_a			  =       s_dict[
														    '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														_a			  =       s_dict[
														    '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()   ):
														if "expert" in key:
																					_a			  =       s_dict[key].shape[0]
																					_a			  =       s_dict[key]
																					for idx in range(lowerCAmelCase__   ):
																												_a			  =       expert_weihts[idx]
																												print(f'{key} -> {key.replace("expert/"				,		"nested fstring"   )}'   )

																					s_dict.pop(lowerCAmelCase__   )

							return s_dict


_snake_case   :   List[str]     							=	{
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}
def 	snake_case_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							import regex as re

							with open(lowerCAmelCase__				,		'''r'''   ) as f:
														_a			  =       f.read()

							_a			  =       re.findall(R'''(.*) = ([0-9.]*)'''				,		lowerCAmelCase__   )
							_a			  =       {}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					_a			  =       float(lowerCAmelCase__   ) if '''.''' in value else int(lowerCAmelCase__   )

							_a			  =       re.findall(R'''(.*activations) = \(\'(.*)\',\)'''				,		lowerCAmelCase__   )[0]
							_a			  =       str(activation[1]   )

							_a			  =       num_experts
							_a			  =       SwitchTransformersConfig(**lowerCAmelCase__   )
							return config
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Any				,		UpperCamelCase :						Dict=None				,		UpperCamelCase :						List[Any]="./"				,		UpperCamelCase :						int=8   ):





							'''simple docstring'''


							print(f'Loading flax weights from : {flax_checkpoint_path}'   )
							_a			  =       checkpoints.load_tax_checkpoint(lowerCAmelCase__   )

							if gin_file is not None:
														_a			  =       convert_gin_to_config(lowerCAmelCase__				,		lowerCAmelCase__   )
							else:
														_a			  =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase__   )

							_a			  =       SwitchTransformersForConditionalGeneration(lowerCAmelCase__   )

							_a			  =       flax_params['''target''']
							_a			  =       flatten_dict(lowerCAmelCase__				,		sep='''/'''   )
							_a			  =       rename_keys(lowerCAmelCase__   )
							_a			  =       unflatten_dict(lowerCAmelCase__				,		sep='''/'''   )

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(lowerCAmelCase__				,		lowerCAmelCase__   )

							print(f'Save PyTorch model to {pytorch_dump_path}'   )
							pt_model.save_pretrained(lowerCAmelCase__   )


if __name__ == "__main__":
				_snake_case   :   Any     							=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--switch_t5x_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
				        ' model architecture. If not provided, a `gin_file` has to be provided.'
				    ),
				)
				parser.add_argument(
				    '--gin_file',
				    default=None,
				    type=str,
				    required=False,
				    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
				)
				parser.add_argument(
				    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
				)
				parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
				_snake_case   :   str     							=	parser.parse_args()
				convert_flax_checkpoint_to_pytorch(
				    args.switch_tax_checkpoint_path,
				    args.config_name,
				    args.gin_file,
				    args.pytorch_dump_folder_path,
				    args.num_experts,
				)







'''simple docstring'''


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_snake_case   :   Optional[Any]     							=	'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_snake_case   :   Optional[int]     							=	'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_snake_case   :   Dict     							=	{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

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







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														return datasets.MetricInfo(
														    description=_DESCRIPTION							,    citation=_CITATION							,    homepage='''https://github.com/google-research/bleurt'''							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
														        {
														            '''predictions''': datasets.Value('''string'''							,    id='''sequence'''      ),
														            '''references''': datasets.Value('''string'''							,    id='''sequence'''      ),
														        }      )							,    codebase_urls=['''https://github.com/google-research/bleurt''']							,    reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']							,    )







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""simple docstring"""


														if self.config_name == "default":
																					logger.warning(
																					    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
																					    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''      )
																					_a			  =       '''bleurt-base-128'''

														if self.config_name.lower() in CHECKPOINT_URLS:
																					_a			  =       self.config_name.lower()

														elif self.config_name.upper() in CHECKPOINT_URLS:
																					_a			  =       self.config_name.upper()

														else:
																					raise KeyError(
																					    F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'      )

														# download the model checkpoint specified by self.config_name and set up the scorer
														_a			  =       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
														_a			  =       score.BleurtScorer(os.path.join(lowerCAmelCase_							,    lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.scorer.score(references=lowerCAmelCase_							,    candidates=lowerCAmelCase_      )
														return {"scores": scores}

