




'''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							:				Optional[Any]			     =	(30, 487)
A							:				Optional[Any]			     =	(36, 1_024)
A							:				Tuple			     =	(100, 100)
A							:				Optional[Any]			     =	100

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

A							:				List[Any]			     =	os.path.split(__file__)
A							:				Union[str, Any]			     =	os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def     snake_case_  (       a__		:     List[str]   ,a__		:     List[Any]   ,a__		:     Any      ):



    """simple docstring"""




    with ArrowWriter(features=snake_case__   ,path=os.path.join(snake_case__   ,"""beta.arrow"""      )      ) as writer:
        for key, record in dummy_data:
            __lowercase          =						my_features.encode_example(snake_case__      )
            writer.write(snake_case__      )
        __lowercase    ,__lowercase          =						writer.finalize()
@get_duration
def     snake_case_  (       a__		:     str   ,a__		:     Optional[int]      ):



    """simple docstring"""




    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    for _ in dataset:
        pass
@get_duration
def     snake_case_  (       a__		:     List[str]   ,a__		:     List[Any]      ):



    """simple docstring"""




    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    dataset.set_format("""numpy"""      )
    for _ in dataset:
        pass
@get_duration
def     snake_case_  (       a__		:     Optional[Any]   ,a__		:     Tuple      ):



    """simple docstring"""




    __lowercase          =						10
    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    for i in range(0   ,len(snake_case__      )   ,snake_case__      ):
        __lowercase          =						dataset[i : i + batch_size]
@get_duration
def     snake_case_  (       a__		:     str   ,a__		:     Dict      ):



    """simple docstring"""




    __lowercase          =						10
    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    dataset.set_format("""numpy"""      )
    for i in range(0   ,len(snake_case__      )   ,snake_case__      ):
        __lowercase          =						dataset[i : i + batch_size]
@get_duration
def     snake_case_  (       a__		:     List[Any]   ,a__		:     Optional[int]      ):



    """simple docstring"""




    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    for col in feats:
        __lowercase          =						dataset[col]
@get_duration
def     snake_case_  (       a__		:     int   ,a__		:     Any      ):



    """simple docstring"""




    __lowercase          =						datasets.Dataset.from_file(
        filename=os.path.join(snake_case__   ,"""beta.arrow"""      )   ,info=datasets.DatasetInfo(features=snake_case__      )      )
    dataset.set_format("""numpy"""      )
    for col in feats:
        __lowercase          =						dataset[col]
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						{}
    __lowercase          =						(
        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:
        __lowercase          =						datasets.Features({"""image""": ArrayaD(snake_case__   ,dtype="""float32"""      )}      )
        __lowercase          =						generate_examples(features=snake_case__   ,num_examples=snake_case__      )
        __lowercase          =						write(snake_case__   ,snake_case__   ,snake_case__      )
        for read_func in read_functions:
            __lowercase          =						read_func(snake_case__   ,snake_case__      )

    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])}
        # )
        __lowercase          =						datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""      )      )      )}      )
        __lowercase          =						generate_examples(
            features=snake_case__   ,num_examples=snake_case__   ,seq_shapes={"""image""": SPEED_TEST_SHAPE}      )
        __lowercase          =						write(snake_case__   ,snake_case__   ,snake_case__      )
        for read_func in read_functions:
            __lowercase          =						read_func(snake_case__   ,snake_case__      )

    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])}
        # )
        __lowercase          =						datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""      )      )}      )
        __lowercase          =						generate_examples(
            features=snake_case__   ,num_examples=snake_case__   ,seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,)
        __lowercase          =						write(snake_case__   ,snake_case__   ,snake_case__      )
        for read_func in read_functions:
            __lowercase          =						read_func(snake_case__   ,snake_case__      )

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


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










'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


A							:				Dict			     =	"""Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""






class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					List[Any]   = """google/flan-t5-base"""
       snake_case_			:					int   = (
           """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
           """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
       )
       snake_case_			:					Optional[int]   = """text_qa"""
       snake_case_			:					List[Any]   = AutoTokenizer
       snake_case_			:					List[str]   = AutoModelForSeqaSeqLM

       snake_case_			:					int   = ["""text""", """text"""]
       snake_case_			:					Dict   = ["""text"""]

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						QA_PROMPT.format(text=lowerCamelCase__ ,		question=lowerCamelCase__					)
           return self.pre_processor(lowerCamelCase__ ,		return_tensors="""pt"""					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> int:

           """simple docstring"""



           __lowercase          =						self.model.generate(**lowerCamelCase__					)

           __lowercase    ,__lowercase          =						inputs["""input_ids"""].shape
           __lowercase          =						output_ids.shape[0]

           return output_ids.reshape(lowerCamelCase__ ,		out_b // in_b ,		*output_ids.shape[1:]					)[0][0]




       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



           return self.pre_processor.decode(lowerCamelCase__ ,		skip_special_tokens=lowerCamelCase__ ,		clean_up_tokenization_spaces=lowerCamelCase__					)




