






'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A      :     List[Any]       	=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A      :     List[str]       	=[]
for i in range(6):
			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
			        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
			    )
			)
			rename_keys.append(
			    (
			        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
			        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
			    )
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
			)
			rename_keys.append(
			    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
			)
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
			rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Optional[Any]:
							lowerCamelCase__   :      int			    =			state_dict.pop(UpperCamelCase )
							lowerCamelCase__   :      Union[str, Any]			    =			val





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			List[Any]:
							lowerCamelCase__   :      Dict			    =			OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase__   :      List[Any]			    =			key.replace("""backbone.0.body"""							,     """backbone.conv_encoder.model""" )
																					lowerCamelCase__   :      int			    =			value
														else:
																					lowerCamelCase__   :      List[str]			    =			value

							return new_state_dict





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase=False ) ->			List[str]:
							lowerCamelCase__   :      List[str]			    =			""""""
							if is_panoptic:
														lowerCamelCase__   :      Union[str, Any]			    =			"""detr."""

							# first: transformer encoder
							for i in range(6 ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowerCamelCase__   :      Union[str, Any]			    =			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
														lowerCamelCase__   :      Union[str, Any]			    =			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__   :      Dict			    =			in_proj_weight[:256, :]
														lowerCamelCase__   :      List[Any]			    =			in_proj_bias[:256]
														lowerCamelCase__   :      List[str]			    =			in_proj_weight[256:512, :]
														lowerCamelCase__   :      Dict			    =			in_proj_bias[256:512]
														lowerCamelCase__   :      Any			    =			in_proj_weight[-256:, :]
														lowerCamelCase__   :      List[Any]			    =			in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6 ):
														# read in weights + bias of input projection layer of self-attention
														lowerCamelCase__   :      List[Any]			    =			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
														lowerCamelCase__   :      Optional[Any]			    =			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__   :      Optional[Any]			    =			in_proj_weight[:256, :]
														lowerCamelCase__   :      str			    =			in_proj_bias[:256]
														lowerCamelCase__   :      Tuple			    =			in_proj_weight[256:512, :]
														lowerCamelCase__   :      Tuple			    =			in_proj_bias[256:512]
														lowerCamelCase__   :      Dict			    =			in_proj_weight[-256:, :]
														lowerCamelCase__   :      str			    =			in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														lowerCamelCase__   :      str			    =			state_dict.pop(
														    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
														lowerCamelCase__   :      Dict			    =			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														lowerCamelCase__   :      Dict			    =			in_proj_weight_cross_attn[:256, :]
														lowerCamelCase__   :      int			    =			in_proj_bias_cross_attn[:256]
														lowerCamelCase__   :      List[str]			    =			in_proj_weight_cross_attn[256:512, :]
														lowerCamelCase__   :      Dict			    =			in_proj_bias_cross_attn[256:512]
														lowerCamelCase__   :      int			    =			in_proj_weight_cross_attn[-256:, :]
														lowerCamelCase__   :      str			    =			in_proj_bias_cross_attn[-256:]





def 					SCREAMING_SNAKE_CASE_			() ->			Dict:
							lowerCamelCase__   :      str			    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__   :      Optional[int]			    =			Image.open(requests.get(UpperCamelCase							,     stream=UpperCamelCase ).raw )

							return im





@torch.no_grad()
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[Any]:
							lowerCamelCase__   :      Dict			    =			DetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowerCamelCase__   :      List[str]			    =			"""resnet101"""
							if "dc5" in model_name:
														lowerCamelCase__   :      int			    =			True
							lowerCamelCase__   :      str			    =			"""panoptic""" in model_name
							if is_panoptic:
														lowerCamelCase__   :      Optional[Any]			    =			250
							else:
														lowerCamelCase__   :      Optional[int]			    =			91
														lowerCamelCase__   :      Tuple			    =			"""huggingface/label-files"""
														lowerCamelCase__   :      Union[str, Any]			    =			"""coco-detection-id2label.json"""
														lowerCamelCase__   :      Dict			    =			json.load(open(hf_hub_download(UpperCamelCase							,     UpperCamelCase							,     repo_type="""dataset""" )							,     """r""" ) )
														lowerCamelCase__   :      Optional[Any]			    =			{int(UpperCamelCase ): v for k, v in idalabel.items()}
														lowerCamelCase__   :      Union[str, Any]			    =			idalabel
														lowerCamelCase__   :      Optional[int]			    =			{v: k for k, v in idalabel.items()}

							# load image processor
							lowerCamelCase__   :      List[Any]			    =			"""coco_panoptic""" if is_panoptic else """coco_detection"""
							lowerCamelCase__   :      Union[str, Any]			    =			DetrImageProcessor(format=UpperCamelCase )

							# prepare image
							lowerCamelCase__   :      List[Any]			    =			prepare_img()
							lowerCamelCase__   :      List[Any]			    =			image_processor(images=UpperCamelCase							,     return_tensors="""pt""" )
							lowerCamelCase__   :      List[str]			    =			encoding["""pixel_values"""]

							logger.info(f'''Converting model {model_name}...''' )

							# load original model from torch hub
							lowerCamelCase__   :      List[Any]			    =			torch.hub.load("""facebookresearch/detr"""							,     UpperCamelCase							,     pretrained=UpperCamelCase ).eval()
							lowerCamelCase__   :      Optional[Any]			    =			detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					lowerCamelCase__   :      Tuple			    =			"""detr.""" + src
														rename_key(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase )
							lowerCamelCase__   :      Optional[Any]			    =			rename_backbone_keys(UpperCamelCase )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase							,     is_panoptic=UpperCamelCase )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCamelCase__   :      Any			    =			"""detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""detr""" )
																					    and not key.startswith("""class_labels_classifier""" )
																					    and not key.startswith("""bbox_predictor""" )
																					):
																												lowerCamelCase__   :      str			    =			state_dict.pop(UpperCamelCase )
																												lowerCamelCase__   :      List[str]			    =			val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowerCamelCase__   :      Optional[int]			    =			state_dict.pop(UpperCamelCase )
																												lowerCamelCase__   :      Dict			    =			val
																					elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
																												continue
																					else:
																												lowerCamelCase__   :      List[str]			    =			state_dict.pop(UpperCamelCase )
																												lowerCamelCase__   :      int			    =			val
														else:
																					if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																												lowerCamelCase__   :      Optional[int]			    =			state_dict.pop(UpperCamelCase )
																												lowerCamelCase__   :      Dict			    =			val
    # finally, create HuggingFace model and load state dict
							lowerCamelCase__   :      List[str]			    =			DetrForSegmentation(UpperCamelCase ) if is_panoptic else DetrForObjectDetection(UpperCamelCase )
							model.load_state_dict(UpperCamelCase )
							model.eval()
							# verify our conversion
							lowerCamelCase__   :      Tuple			    =			detr(UpperCamelCase )
							lowerCamelCase__   :      Optional[Any]			    =			model(UpperCamelCase )
							assert torch.allclose(outputs.logits							,     original_outputs["""pred_logits"""]							,     atol=1E-4 )
							assert torch.allclose(outputs.pred_boxes							,     original_outputs["""pred_boxes"""]							,     atol=1E-4 )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks							,     original_outputs["""pred_masks"""]							,     atol=1E-4 )

							# Save model and image processor
							logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
							Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
							model.save_pretrained(UpperCamelCase )
							image_processor.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
			_A      :     int       	=argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR 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.'''
			)
			_A      :     Any       	=parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''







import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Dict:
							assert isinstance(UpperCamelCase							,     UpperCamelCase )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      int			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      List[Any]			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      Union[str, Any]			    =			CsvDatasetReader(UpperCamelCase							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[Any]			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     split=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )
							assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""							,     [str, list] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							if issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      int			    =			csv_path
							elif issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      Tuple			    =			[csv_path]
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      str			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=("train",) ) ->			int:
							assert isinstance(UpperCamelCase							,     UpperCamelCase )
							for split in splits:
														lowerCamelCase__   :      Optional[Any]			    =			dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 3
														assert dataset.column_names == ["col_1", "col_2", "col_3"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Optional[Any]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Dict			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__   :      int			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      Optional[Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[str]			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      Tuple			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      str			    =			CsvDatasetReader({"""train""": csv_path}							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							if split:
														lowerCamelCase__   :      Tuple			    =			{split: csv_path}
							else:
														lowerCamelCase__   :      Union[str, Any]			    =			{"""train""": csv_path, """test""": csv_path}
							lowerCamelCase__   :      Dict			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Tuple			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase							,     splits=list(path.keys() ) )
							assert all(dataset[split].split == split for split in path.keys() )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Tuple:
							with open(UpperCamelCase							,     encoding="""utf-8""" ) as csvfile:
														yield from csv.reader(UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      Union[str, Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[Any]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=1 ).write()

							lowerCamelCase__   :      Optional[Any]			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      str			    =			iter_csv_file(UpperCamelCase )

							for rowa, rowa in zip(UpperCamelCase							,     UpperCamelCase ):
														assert rowa == rowa





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			List[str]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[int]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=2 ).write()

							lowerCamelCase__   :      Tuple			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      Union[str, Any]			    =			iter_csv_file(UpperCamelCase )

							for rowa, rowa in zip(UpperCamelCase							,     UpperCamelCase ):
														assert rowa == rowa





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[Any]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							with pytest.raises(UpperCamelCase ):
														CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=0 )

