

"""simple docstring"""
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


A									=						(30, 487)
A									=						(36, 1024)
A									=						(100, 100)
A									=						100

A									=						datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

A			,       A									=						os.path.split(__file__)
A									=						os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					int   )						->     Tuple:



       """simple docstring"""







       with ArrowWriter(features=lowerCamelCase     , path=os.path.join(lowerCamelCase     , '''beta.arrow'''   )   ) as writer:
              for key, record in dummy_data:
                     __magic_name__			:		str        =     my_features.encode_example(lowerCamelCase   )
                     writer.write(lowerCamelCase   )
              __magic_name__       ,							__magic_name__			:		Optional[Any]        =     writer.finalize()







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]   )						->     Union[str, Any]:



       """simple docstring"""







       __magic_name__			:		Any        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for _ in dataset:
              pass







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					Dict     , lowerCamelCase							:					Optional[int]   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for _ in dataset:
              pass







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					List[Any]   )						->     int:



       """simple docstring"""







       __magic_name__			:		int        =     10
       __magic_name__			:		Union[str, Any]        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for i in range(0     , len(lowerCamelCase   )     , lowerCamelCase   ):
              __magic_name__			:		Optional[int]        =     dataset[i : i + batch_size]







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Optional[int]   )						->     Union[str, Any]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     10
       __magic_name__			:		Any        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for i in range(0     , len(lowerCamelCase   )     , lowerCamelCase   ):
              __magic_name__			:		Tuple        =     dataset[i : i + batch_size]







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					Dict     , lowerCamelCase							:					Dict   )						->     int:



       """simple docstring"""







       __magic_name__			:		List[str]        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for col in feats:
              __magic_name__			:		Union[str, Any]        =     dataset[col]







@get_duration
def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					int   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase     , '''beta.arrow'''   )     , info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for col in feats:
              __magic_name__			:		str        =     dataset[col]







def       UpperCamelCase_					(     )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		Dict        =     {}
       __magic_name__			:		Dict        =     (
           read_unformated,
           read_formatted_as_numpy,
           read_batch_unformated,
           read_batch_formatted_as_numpy,
           read_col_unformated,
           read_col_formatted_as_numpy,
       )
       with tempfile.TemporaryDirectory() as tmp_dir:
              __magic_name__			:		str        =     datasets.Features({'''image''': ArrayaD(lowerCamelCase     , dtype='''float32'''   )}   )
              __magic_name__			:		Any        =     generate_examples(features=lowerCamelCase     , num_examples=lowerCamelCase   )
              __magic_name__			:		Union[str, Any]        =     write(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
              for read_func in read_functions:
                     __magic_name__			:		List[str]        =     read_func(lowerCamelCase     , lowerCamelCase   )

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
              # )
              __magic_name__			:		str        =     datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''   )   )   )}   )
              __magic_name__			:		str        =     generate_examples(
                  features=lowerCamelCase     , num_examples=lowerCamelCase     , seq_shapes={'''image''': SPEED_TEST_SHAPE}   )
              __magic_name__			:		Tuple        =     write(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
              for read_func in read_functions:
                     __magic_name__			:		Dict        =     read_func(lowerCamelCase     , lowerCamelCase   )

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
              # )
              __magic_name__			:		List[Any]        =     datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''   )   )}   )
              __magic_name__			:		Any        =     generate_examples(
                  features=lowerCamelCase     , num_examples=lowerCamelCase     , seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}     , )
              __magic_name__			:		Union[str, Any]        =     write(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
              for read_func in read_functions:
                     __magic_name__			:		Optional[Any]        =     read_func(lowerCamelCase     , lowerCamelCase   )

       with open(lowerCamelCase     , '''wb'''   ) as f:
              f.write(json.dumps(lowerCamelCase   ).encode('''utf-8'''   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_array_xd()



"""simple docstring"""
import argparse

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


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

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


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 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."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
