


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, SwinConfig
from transformers.utils import logging


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


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						SwinConfig(
		    embed_dim=192     , depths=(2, 2, 18, 2)     , num_heads=(6, 12, 24, 48)     , window_size=12     , out_features=["stage2", "stage3", "stage4"]     , )

		SCREAMING_SNAKE_CASE_				:Dict				     =						DetaConfig(
		    backbone_config=a     , num_queries=900     , encoder_ffn_dim=2048     , decoder_ffn_dim=2048     , num_feature_levels=5     , assign_first_stage=a     , with_box_refine=a     , two_stage=a     , )

		# set labels
		SCREAMING_SNAKE_CASE_				:Dict				     =						"huggingface/label-files"
		if "o365" in model_name:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						366
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						"object365-id2label.json"
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						91
				SCREAMING_SNAKE_CASE_				:str				     =						"coco-detection-id2label.json"

		SCREAMING_SNAKE_CASE_				:List[str]				     =						num_labels
		SCREAMING_SNAKE_CASE_				:List[str]				     =						json.load(open(cached_download(hf_hub_url(a     , a     , repo_type="dataset" ) )     , "r" ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{int(a ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						idalabel
		SCREAMING_SNAKE_CASE_				:str				     =						{v: k for k, v in idalabel.items()}

		return config


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]

		# stem
		# fmt: off
		rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
		rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
		rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
		rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
		# stages
		for i in range(len(config.backbone_config.depths ) ):
				for j in range(config.backbone_config.depths[i] ):
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") )

				if i < 3:
						rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight") )
						rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias") )

		rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
		rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
		rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
		rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
		rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
		rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )

		# 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     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						dct.pop(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						val


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
		for i in range(len(backbone_config.depths ) ):
				SCREAMING_SNAKE_CASE_				:int				     =						num_features[i]
				for j in range(backbone_config.depths[i] ):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight" )
						SCREAMING_SNAKE_CASE_				:Tuple				     =						state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias" )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						in_proj_weight[:dim, :]
						SCREAMING_SNAKE_CASE_				:Any				     =						in_proj_bias[: dim]
						SCREAMING_SNAKE_CASE_				:Tuple				     =						in_proj_weight[
						    dim : dim * 2, :
						]
						SCREAMING_SNAKE_CASE_				:Any				     =						in_proj_bias[
						    dim : dim * 2
						]
						SCREAMING_SNAKE_CASE_				:Tuple				     =						in_proj_weight[
						    -dim :, :
						]
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						in_proj_bias[-dim :]
						# fmt: on


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						config.d_model
		for i in range(config.decoder_layers ):
				# read in weights + bias of input projection layer of self-attention
				SCREAMING_SNAKE_CASE_				:Any				     =						state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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_				:Dict				     =						in_proj_weight[:hidden_size, :]
				SCREAMING_SNAKE_CASE_				:Any				     =						in_proj_bias[:hidden_size]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_				:List[str]				     =						in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE_				:Any				     =						in_proj_weight[-hidden_size:, :]
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						in_proj_bias[-hidden_size:]


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						"http://images.cocodataset.org/val2017/000000039769.jpg"
		SCREAMING_SNAKE_CASE_				:Any				     =						Image.open(requests.get(a     , stream=a ).raw )

		return im


@torch.no_grad()
def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						get_deta_config(a )

		# load original state dict
		if model_name == "deta-swin-large":
				SCREAMING_SNAKE_CASE_				:Dict				     =						hf_hub_download(repo_id="nielsr/deta-checkpoints"     , filename="adet_swin_ft.pth" )
		elif model_name == "deta-swin-large-o365":
				SCREAMING_SNAKE_CASE_				:str				     =						hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"     , filename="deta_swin_pt_o365.pth" )
		else:
				raise ValueError(F"Model name {model_name} not supported" )

		SCREAMING_SNAKE_CASE_				:int				     =						torch.load(a     , map_location="cpu" )["model"]

		# original state dict
		for name, param in state_dict.items():
				print(a     , param.shape )

		# rename keys
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						create_rename_keys(a )
		for src, dest in rename_keys:
				rename_key(a     , a     , a )
		read_in_swin_q_k_v(a     , config.backbone_config )
		read_in_decoder_q_k_v(a     , a )

		# 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_				:List[Any]				     =						state_dict.pop(a )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						val
				if "input_proj" in key:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						state_dict.pop(a )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						SCREAMING_SNAKE_CASE_				:Dict				     =						state_dict.pop(a )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						val

    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						DetaForObjectDetection(a )
		model.load_state_dict(a )
		model.eval()

		SCREAMING_SNAKE_CASE_				:Tuple				     =						"cuda" if torch.cuda.is_available() else "cpu"
		model.to(a )

		# load image processor
		SCREAMING_SNAKE_CASE_				:Dict				     =						DetaImageProcessor(format="coco_detection" )

		# verify our conversion on image
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						prepare_img()
		SCREAMING_SNAKE_CASE_				:Any				     =						processor(images=a     , return_tensors="pt" )
		SCREAMING_SNAKE_CASE_				:List[str]				     =						encoding["pixel_values"]
		SCREAMING_SNAKE_CASE_				:List[str]				     =						model(pixel_values.to(a ) )

		# verify logits
		print("Logits:"     , outputs.logits[0, :3, :3] )
		print("Boxes:"     , outputs.pred_boxes[0, :3, :3] )
		if model_name == "deta-swin-large":
				SCREAMING_SNAKE_CASE_				:int				     =						torch.tensor(
				    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
		elif model_name == "deta-swin-large-o365":
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.tensor(
				    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
		assert torch.allclose(outputs.logits[0, :3, :3]     , expected_logits.to(a )     , atol=1e-4 )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]     , expected_boxes.to(a )     , 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(a ).mkdir(exist_ok=a )
				model.save_pretrained(a )
				processor.save_pretrained(a )

		# 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__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    type=str,
		    default="deta-swin-large",
		    choices=["deta-swin-large", "deta-swin-large-o365"],
		    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."
		)
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     lowercase	(    a ):



		'''simple docstring'''




		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(a ):
						return ext

		raise Exception(
		    F"Unable to determine file format from file extension {path}. "
		    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}" )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				:str				     =						PipelineDataFormat.from_str(
		    format=a     , output_path=args.output     , input_path=args.input     , column=args.column if args.column else nlp.default_input_names     , overwrite=args.overwrite     , )
		return RunCommand(a     , a )



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				:Any				     =						parser.add_parser("run"   ,  help="Run a pipeline through the CLI")
				run_parser.add_argument("--task"   ,  choices=get_supported_tasks()   ,  help="Task to run")
				run_parser.add_argument("--input"   ,  type=UpperCAmelCase   ,  help="Path to the file to use for inference")
				run_parser.add_argument("--output"   ,  type=UpperCAmelCase   ,  help="Path to the file that will be used post to write results.")
				run_parser.add_argument("--model"   ,  type=UpperCAmelCase   ,  help="Name or path to the model to instantiate.")
				run_parser.add_argument("--config"   ,  type=UpperCAmelCase   ,  help="Name or path to the model's config to instantiate.")
				run_parser.add_argument(
				    "--tokenizer"   ,  type=UpperCAmelCase   ,  help="Name of the tokenizer to use. (default: same as the model name)")
				run_parser.add_argument(
				    "--column"   ,  type=UpperCAmelCase   ,  help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"   ,  )
				run_parser.add_argument(
				    "--format"   ,  type=UpperCAmelCase   ,  default="infer"   ,  choices=PipelineDataFormat.SUPPORTED_FORMATS   ,  help="Input format to read from"   ,  )
				run_parser.add_argument(
				    "--device"   ,  type=UpperCAmelCase   ,  default=-1   ,  help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"   ,  )
				run_parser.add_argument("--overwrite"   ,  action="store_true"   ,  help="Allow overwriting the output file.")
				run_parser.set_defaults(func=UpperCAmelCase)







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						nlp(**UpperCAmelCase) if self._reader.is_multi_columns else nlp(UpperCAmelCase)
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								outputs.append(UpperCAmelCase)
						else:
								outputs += output

        # Saving data
				if self._nlp.binary_output:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self._reader.save_binary(UpperCAmelCase)
						logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}")
				else:
						self._reader.save(UpperCAmelCase)






