
"""simple docstring"""




from argparse import ArgumentParser

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


UpperCAmelCase__      :    Optional[int]    							= logging.get_logger(__name__)  # pylint: disable=invalid-name

def lowercase_  (   _snake_case						):
							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(_snake_case						):
																					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_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   pipeline(
							    task=args.task   ,model=args.model if args.model else None   ,config=args.config   ,tokenizer=args.tokenizer   ,device=args.device   ,)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   try_infer_format_from_ext(args.input						) if args.format == """infer""" else args.format
							SCREAMING_SNAKE_CASE__						:       Tuple           =   PipelineDataFormat.from_str(
							    format=_snake_case   ,output_path=args.output   ,input_path=args.input   ,column=args.column if args.column else nlp.default_input_names   ,overwrite=args.overwrite   ,)
							return RunCommand(_snake_case   ,_snake_case						)



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nlp
											SCREAMING_SNAKE_CASE__						:       Tuple           =   reader



				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[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=SCREAMING_SNAKE_CASE__   ,       help="""Path to the file to use for inference"""    )
											run_parser.add_argument("""--output"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Path to the file that will be used post to write results."""    )
											run_parser.add_argument("""--model"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name or path to the model to instantiate."""    )
											run_parser.add_argument("""--config"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name or path to the model's config to instantiate."""    )
											run_parser.add_argument(
											    """--tokenizer"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name of the tokenizer to use. (default: same as the model name)"""    )
											run_parser.add_argument(
											    """--column"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""   ,       )
											run_parser.add_argument(
											    """--format"""   ,       type=SCREAMING_SNAKE_CASE__   ,       default="""infer"""   ,       choices=PipelineDataFormat.SUPPORTED_FORMATS   ,       help="""Input format to read from"""   ,       )
											run_parser.add_argument(
											    """--device"""   ,       type=SCREAMING_SNAKE_CASE__   ,       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=SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self._nlp, []

											for entry in self._reader:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   nlp(**SCREAMING_SNAKE_CASE__    ) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE__    )
																		if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									outputs.append(SCREAMING_SNAKE_CASE__    )
																		else:
																									outputs += output

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






"""simple docstring"""




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return super().__call__(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




