
"""simple docstring"""



import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Optional[Any]="attention"						):






							"""simple docstring"""



							_lowerCAmelCase  	=			_lowerCAmelCase  	=			np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]						)
							_lowerCAmelCase  	=			k_tmp.reshape(k_tmp.shape[0]     ,  k_tmp.shape[1] * k_tmp.shape[2]						)
							_lowerCAmelCase  	=			np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]						)
							_lowerCAmelCase  	=			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]     ,  o_tmp.shape[2]						)
							_lowerCAmelCase  	=			np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]						)
							_lowerCAmelCase  	=			q_tmp.reshape(q_tmp.shape[0]     ,  q_tmp.shape[1] * q_tmp.shape[2]						)
							_lowerCAmelCase  	=			np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]						)
							_lowerCAmelCase  	=			v_tmp.reshape(v_tmp.shape[0]     ,  v_tmp.shape[1] * v_tmp.shape[2]						)
							return k, o, q, v






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					List[str]=False						):






							"""simple docstring"""



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

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					dict     ,  *, SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					bool     ,  SCREAMING_SNAKE_CASE:					bool = False						):






							"""simple docstring"""



							_lowerCAmelCase  	=			traverse_util.flatten_dict(variables['target']						)
							_lowerCAmelCase  	=			{'/'.join(SCREAMING_SNAKE_CASE						): v for k, v in old.items()}

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

							_lowerCAmelCase  	=			collections.OrderedDict()

							# Shared embeddings.
							_lowerCAmelCase  	=			old['token_embedder/embedding']

							# Encoder.
							for i in range(SCREAMING_SNAKE_CASE						):
														# Block i, layer 0 (Self Attention).
														_lowerCAmelCase  	=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'encoder'     ,  'pre_attention_layer_norm'						)
														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			tax_attention_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'encoder'     ,  'attention'						)
														_lowerCAmelCase  	=			layer_norm
														_lowerCAmelCase  	=			k.T
														_lowerCAmelCase  	=			o.T
														_lowerCAmelCase  	=			q.T
														_lowerCAmelCase  	=			v.T

														# Block i, layer 1 (MLP).
														_lowerCAmelCase  	=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'encoder'     ,  'pre_mlp_layer_norm'						)
														_lowerCAmelCase	,		_lowerCAmelCase  	=			tax_mlp_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'encoder'     ,  SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			layer_norm
														if split_mlp_wi:
																					_lowerCAmelCase  	=			wi[0].T
																					_lowerCAmelCase  	=			wi[1].T
														else:
																					_lowerCAmelCase  	=			wi.T
														_lowerCAmelCase  	=			wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					_lowerCAmelCase  	=			tax_relpos_bias_lookup(
																					    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'encoder'						).T

							_lowerCAmelCase  	=			old['encoder/encoder_norm/scale']

							if not scalable_attention:
														_lowerCAmelCase  	=			tax_relpos_bias_lookup(
														    SCREAMING_SNAKE_CASE     ,  0     ,  'encoder'						).T
														_lowerCAmelCase  	=			tax_relpos_bias_lookup(
														    SCREAMING_SNAKE_CASE     ,  0     ,  'decoder'						).T

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

																					# Block i, layer 1 (Cross Attention).
																					_lowerCAmelCase  	=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'decoder'     ,  'pre_cross_attention_layer_norm'						)
																					_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			tax_attention_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'decoder'     ,  'encoder_decoder_attention'						)
																					_lowerCAmelCase  	=			layer_norm
																					_lowerCAmelCase  	=			k.T
																					_lowerCAmelCase  	=			o.T
																					_lowerCAmelCase  	=			q.T
																					_lowerCAmelCase  	=			v.T

																					# Block i, layer 2 (MLP).
																					_lowerCAmelCase  	=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'decoder'     ,  'pre_mlp_layer_norm'						)
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			tax_mlp_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'decoder'     ,  SCREAMING_SNAKE_CASE						)
																					_lowerCAmelCase  	=			layer_norm
																					if split_mlp_wi:
																												_lowerCAmelCase  	=			wi[0].T
																												_lowerCAmelCase  	=			wi[1].T
																					else:
																												_lowerCAmelCase  	=			wi.T
																					_lowerCAmelCase  	=			wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												_lowerCAmelCase  	=			tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  'decoder'						).T

														_lowerCAmelCase  	=			old['decoder/decoder_norm/scale']

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					_lowerCAmelCase  	=			old['decoder/logits_dense/kernel'].T

							return new






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					bool						):






							"""simple docstring"""



							_lowerCAmelCase  	=			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:
														_lowerCAmelCase  	=			state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					_lowerCAmelCase  	=			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.'						)
																					_lowerCAmelCase  	=			state_dict['shared.weight']

							return state_dict






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			convert_tax_to_pytorch(
							    SCREAMING_SNAKE_CASE     ,  num_layers=config.num_layers     ,  is_encoder_only=SCREAMING_SNAKE_CASE     ,  scalable_attention=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			make_state_dict(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							model.load_state_dict(SCREAMING_SNAKE_CASE     ,  strict=SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  SCREAMING_SNAKE_CASE:					bool = False     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			MTaConfig.from_json_file(SCREAMING_SNAKE_CASE						)
							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:
														_lowerCAmelCase  	=			UMTaEncoderModel(SCREAMING_SNAKE_CASE						)
							else:
														_lowerCAmelCase  	=			UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE						)

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(SCREAMING_SNAKE_CASE						)
							print('Done'						)


if __name__ == "__main__":
				_snake_case          =						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
				)
				parser.add_argument(
				    '''--scalable_attention''',
				    action='''store_true''',
				    help='''Whether the model uses scaled attention (umt5 model)''',
				    default=False,
				)
				_snake_case          =						parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path,
				    args.config_file,
				    args.pytorch_dump_path,
				    args.is_encoder_only,
				    args.scalable_attention,
				)








"""simple docstring"""



import pytest


_snake_case          =						'''__dummy_dataset1__'''

_snake_case          =						'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_loading_script_name
							_lowerCAmelCase  	=			tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			script_dir / f"""{script_name}.py"""
							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							return str(SCREAMING_SNAKE_CASE						)






