





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   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Union[str, Any]  )     ->	int:



					'''simple docstring'''




					return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Dict="attention"  )     ->	int:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    Any=False  )     ->	List[Any]:



					'''simple docstring'''




					if split_mlp_wi:
										_snake_case													=			params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
										_snake_case													=			params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
										_snake_case													=			(wi_a, wi_a)
					else:
										_snake_case													=			params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

					_snake_case													=			params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
					return wi, wo





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    Tuple  )     ->	Optional[Any]:



					'''simple docstring'''




					return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      *, UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    str = False  )     ->	Tuple:



					'''simple docstring'''




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

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

					_snake_case													=			collections.OrderedDict()

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

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

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

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

					if not scalable_attention:
										_snake_case													=			tax_relpos_bias_lookup(
										    lowerCAmelCase_    ,      0    ,      'encoder'  ).T
										_snake_case													=			tax_relpos_bias_lookup(
										    lowerCAmelCase_    ,      0    ,      'decoder'  ).T

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

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

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

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

										_snake_case													=			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:
															_snake_case													=			old['decoder/logits_dense/kernel'].T

					return new





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    List[str]  )     ->	Any:



					'''simple docstring'''




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

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

					return state_dict





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Any  )     ->	Tuple:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    List[str] = False    ,      UpperCamelCase__     :    Tuple = False    ,      )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			MTaConfig.from_json_file(lowerCAmelCase_  )
					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:
										_snake_case													=			UMTaEncoderModel(lowerCAmelCase_  )
					else:
										_snake_case													=			UMTaForConditionalGeneration(lowerCAmelCase_  )

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

					# Save pytorch-model
					print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
					model.save_pretrained(lowerCAmelCase_  )

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


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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,
					)
					UpperCAmelCase_ 							=       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,
					)










import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_ 							=       [
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict=True  )     ->	Dict:



					'''simple docstring'''




					if with_config:
										return [
										    {
										        "testcase_name": d["dataset"] + "/" + d["config_name"],
										        "dataset": d["dataset"],
										        "config_name": d["config_name"],
										    }
										    for d in DATASETS_ON_HF_GCP
										]
					else:
										return [
										    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
										]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowerCamelCase   )   )
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		None
					lowerCAmelCase_							=		None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										with TemporaryDirectory() as tmp_dir:
															_snake_case													=			dataset_module_factory(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)

															_snake_case													=			import_main_class(dataset_module.module_path			,  dataset=lowerCAmelCase_					)

															_snake_case													=			builder_cls(
															    cache_dir=lowerCAmelCase_			,  config_name=lowerCAmelCase_			,  hash=dataset_module.hash			,  )

															_snake_case													=			'/'.join(
															    [
															        HF_GCP_BASE_URL,
															        builder_instance._relative_data_dir(with_hash=lowerCAmelCase_					).replace(os.sep			,  '/'					),
															        config.DATASET_INFO_FILENAME,
															    ]					)
															_snake_case													=			cached_path(lowerCAmelCase_			,  cache_dir=lowerCAmelCase_					)
															self.assertTrue(os.path.exists(lowerCAmelCase_					)					)





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Any  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
					_snake_case													=			None
					builder_instance.download_and_prepare()
					_snake_case													=			builder_instance.as_dataset()
					assert ds





@pytest.mark.integration
def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			dataset_module_factory('wikipedia'    ,      cache_dir=UpperCamelCase__  )
					_snake_case													=			import_main_class(dataset_module.module_path    ,      dataset=UpperCamelCase__  )
					_snake_case													=			builder_cls(
					    cache_dir=UpperCamelCase__    ,      config_name='20220301.frr'    ,      hash=dataset_module.hash    ,      )
					_snake_case													=			builder_instance.as_streaming_dataset()
					assert ds
					assert isinstance(UpperCamelCase__    ,      UpperCamelCase__  )
					assert "train" in ds
					assert isinstance(ds['train']    ,      UpperCamelCase__  )
					assert next(iter(ds['train']  )  )



