




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  						=       logging.get_logger(__name__)



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       =DetaConfig(
        num_queries=900,    encoder_ffn_dim=2_048,    decoder_ffn_dim=2_048,    num_feature_levels=5,    assign_first_stage=__UpperCamelCase,    with_box_refine=__UpperCamelCase,    two_stage=__UpperCamelCase,    )

    # set labels
    SCREAMING_SNAKE_CASE__       =91
    SCREAMING_SNAKE_CASE__       ="""huggingface/label-files"""
    SCREAMING_SNAKE_CASE__       ="""coco-detection-id2label.json"""
    SCREAMING_SNAKE_CASE__       =json.load(open(cached_download(hf_hub_url(__UpperCamelCase,    __UpperCamelCase,    repo_type="""dataset"""     )     ),    """r"""     )     )
    SCREAMING_SNAKE_CASE__       ={int(__UpperCamelCase     ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE__       =idalabel
    SCREAMING_SNAKE_CASE__       ={v: k for k, v in idalabel.items()}

    return config



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[]

    # stem
    # fmt: off
    rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")     )
    rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")     )
    rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")     )
    rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")     )
    rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")     )
    # stages
    for stage_idx in range(len(config.backbone_config.depths     )     ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                    )     )
            # 3 convs
            for i in range(3     ):
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                    )     )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                    )     )
    # transformer encoder
    for i in range(config.encoder_layers     ):
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")     )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")     )

    # transformer decoder
    for i in range(config.decoder_layers     ):
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")     )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")     )

    # fmt: on

    return rename_keys



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =dct.pop(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =val



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    # transformer decoder self-attention layers
    SCREAMING_SNAKE_CASE__       =config.d_model
    for i in range(config.decoder_layers     ):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE__       =state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
        SCREAMING_SNAKE_CASE__       =state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE__       =in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__       =in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE__       =in_proj_bias[-hidden_size:]



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
    SCREAMING_SNAKE_CASE__       =Image.open(requests.get(__UpperCamelCase,    stream=__UpperCamelCase     ).raw     )

    return im



@torch.no_grad()
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =get_deta_config()

    # load original state dict
    if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__       ="""adet_checkpoint0011.pth"""
    elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__       ="""adet_2x_checkpoint0023.pth"""
    else:
        raise ValueError(f"""Model name {model_name} not supported"""     )
    SCREAMING_SNAKE_CASE__       =hf_hub_download(repo_id="""nielsr/deta-checkpoints""",    filename=__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =torch.load(__UpperCamelCase,    map_location="""cpu"""     )["""model"""]

    # rename keys
    SCREAMING_SNAKE_CASE__       =create_rename_keys(__UpperCamelCase     )
    for src, dest in rename_keys:
        rename_key(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    read_in_decoder_q_k_v(__UpperCamelCase,    __UpperCamelCase     )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
            SCREAMING_SNAKE_CASE__       =val
        if "input_proj" in key:
            SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
            SCREAMING_SNAKE_CASE__       =val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            SCREAMING_SNAKE_CASE__       =state_dict.pop(__UpperCamelCase     )
            SCREAMING_SNAKE_CASE__       =val

    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE__       =DetaForObjectDetection(__UpperCamelCase     )
    model.load_state_dict(__UpperCamelCase     )
    model.eval()

    SCREAMING_SNAKE_CASE__       ="""cuda""" if torch.cuda.is_available() else """cpu"""
    model.to(__UpperCamelCase     )

    # load image processor
    SCREAMING_SNAKE_CASE__       =DetaImageProcessor(format="""coco_detection"""     )

    # verify our conversion on image
    SCREAMING_SNAKE_CASE__       =prepare_img()
    SCREAMING_SNAKE_CASE__       =processor(images=__UpperCamelCase,    return_tensors="""pt"""     )
    SCREAMING_SNAKE_CASE__       =encoding["""pixel_values"""]
    SCREAMING_SNAKE_CASE__       =model(pixel_values.to(__UpperCamelCase     )     )

    # verify logits
    if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__       =torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]     )
        SCREAMING_SNAKE_CASE__       =torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]     )
    elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__       =torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]     )
        SCREAMING_SNAKE_CASE__       =torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]     )

    assert torch.allclose(outputs.logits[0, :3, :3],    expected_logits.to(__UpperCamelCase     ),    atol=1E-4     )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3],    expected_boxes.to(__UpperCamelCase     ),    atol=1E-4     )
    print("""Everything ok!"""     )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""     )
        Path(__UpperCamelCase     ).mkdir(exist_ok=__UpperCamelCase     )
        model.save_pretrained(__UpperCamelCase     )
        processor.save_pretrained(__UpperCamelCase     )

    # Push to hub
    if push_to_hub:
        print("""Pushing model and processor to hub..."""     )
        model.push_to_hub(f"""jozhang97/{model_name}"""     )
        processor.push_to_hub(f"""jozhang97/{model_name}"""     )


if __name__ == "__main__":
    lowerCamelCase_  						=       argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        type=str,
        default="deta-resnet-50",
        choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        help="Path to the folder to output PyTorch model.",
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )
    lowerCamelCase_  						=       parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]=False						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(
             filename_skip_take["""filename"""],
             filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
             filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
         )
         open(os.path.join(_UpperCamelCase						)    ,"""wb"""						).close()
         SCREAMING_SNAKE_CASE__       =pa.Table.from_pydict({"""filename""": [Path(_UpperCamelCase						).name] * 1_0_0}						)
         if take == -1:
             SCREAMING_SNAKE_CASE__       =len(_UpperCamelCase						) - skip
         if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE__       =pa_table.slice(_UpperCamelCase    ,_UpperCamelCase						)
         return pa_table






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			List[str]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =[train_info, test_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""test[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-test"""						)
             self.assertEqual(dset.num_rows    ,3_3						)
             self.assertEqual(dset.num_columns    ,1						)

             SCREAMING_SNAKE_CASE__       =["""train""", """test[:33%]"""]
             SCREAMING_SNAKE_CASE__       =[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
             for instructions in [instructionsa, instructionsa]:
                 SCREAMING_SNAKE_CASE__       =[reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						) for instr in instructions]
                 SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(Dataset(**_UpperCamelCase						) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset["""filename"""][0]    ,f"""{name}-train"""						)
                 self.assertEqual(train_dset.num_rows    ,1_0_0						)
                 self.assertEqual(train_dset.num_columns    ,1						)
                 self.assertIsInstance(train_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(train_dset.split						)    ,"""train"""						)
                 self.assertEqual(test_dset["""filename"""][0]    ,f"""{name}-test"""						)
                 self.assertEqual(test_dset.num_rows    ,3_3						)
                 self.assertEqual(test_dset.num_columns    ,1						)
                 self.assertIsInstance(test_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(test_dset.split						)    ,"""test[:33%]"""						)
                 del train_dset, test_dset




     def   __A    (     self      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0_0    ,shard_lengths=[1_0_0] * 1_0						)
         SCREAMING_SNAKE_CASE__       =[train_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-train-00000-of-00010"""						)
             self.assertEqual(dset["""filename"""][-1]    ,f"""{name}-train-00003-of-00010"""						)
             self.assertEqual(dset.num_rows    ,3_3_0						)
             self.assertEqual(dset.num_columns    ,1						)




     def   __A    (     self      :			Dict						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       =[
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""train"""						)},
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""test"""						), """skip""": 1_0, """take""": 1_0},
             ]
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read_files(_UpperCamelCase    ,original_instructions="""train+test[10:20]"""						)						)
             self.assertEqual(dset.num_rows    ,1_1_0						)
             self.assertEqual(dset.num_columns    ,1						)
             del dset






@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =ArrowReader.read_table(__UpperCamelCase,    in_memory=__UpperCamelCase     )
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    SCREAMING_SNAKE_CASE__       =ArrowReader("""""",    __UpperCamelCase     )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =reader.read_files([{"""filename""": filename}],    in_memory=__UpperCamelCase     )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    SCREAMING_SNAKE_CASE__       =dataset_kwargs["""arrow_table"""]
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



def      UpperCAmelCase_   (     ):
    assert ReadInstruction("""train""",    to=10,    unit="""abs"""     ).to_spec() == "train[:10]"
    assert ReadInstruction("""train""",    from_=-80,    to=10,    unit="""%"""     ).to_spec() == "train[-80%:10%]"

    SCREAMING_SNAKE_CASE__       ="""train+test"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test

    SCREAMING_SNAKE_CASE__       ="""train[2:10]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_abs

    SCREAMING_SNAKE_CASE__       ="""train[15%:-20%]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct

    SCREAMING_SNAKE_CASE__       ="""train[:10%](closest)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == "train[:10%]"

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct_rounding

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test_pct_rounding



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""dummy"""
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix"""

    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train.arrow"""     ), "skip": 0, "take": 33}
    ]

    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100,    shard_lengths=[10] * 10     )]
    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00000-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00001-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00002-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00003-of-00010.arrow"""     ), "skip": 0, "take": 3},
    ]




@pytest.mark.parametrize("""name, expected_exception""",    [(None, TypeError), ("""""", ValueError)]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix_path"""
    with pytest.raises(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )






