
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase      =logging.get_logger(__name__)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = IMAGENET_DEFAULT_MEAN						,						__SCREAMING_SNAKE_CASE = IMAGENET_DEFAULT_STD						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				size if size is not None else {'''shortest_edge''': 2_2_4}
           UpperCamelCase__    :   Tuple					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   List[str]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						param_name='''crop_size'''  )

           UpperCamelCase__    :   Any					=				do_resize
           UpperCamelCase__    :   List[Any]					=				size
           UpperCamelCase__    :   List[str]					=				resample
           UpperCamelCase__    :   str					=				do_center_crop
           UpperCamelCase__    :   Dict					=				crop_size
           UpperCamelCase__    :   Dict					=				do_rescale
           UpperCamelCase__    :   Dict					=				rescale_factor
           UpperCamelCase__    :   Union[str, Any]					=				do_normalize
           UpperCamelCase__    :   str					=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           UpperCamelCase__    :   Optional[int]					=				image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                 UpperCamelCase__    :   str					=				int((2_5_6 / 2_2_4) * size['''shortest_edge''']  )
                 UpperCamelCase__    :   Any					=				get_resize_output_image_size(__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   int					=				{'''height''': output_size[0], '''width''': output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                 raise ValueError(
                     F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''  )
           return resize(
               __SCREAMING_SNAKE_CASE						,						size=(size_dict['''height'''], size_dict['''width'''])						,						resample=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
           return center_crop(__SCREAMING_SNAKE_CASE						,						size=(size['''height'''], size['''width'''])						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   BatchFeature:




           """simple docstring"""







           UpperCamelCase__    :   int					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   Any					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   Tuple					=				do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase__    :   List[Any]					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   int					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   int					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Tuple					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Dict					=				image_std if image_std is not None else self.image_std

           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   int					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						default_to_square=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__    :   Tuple					=				get_size_dict(__SCREAMING_SNAKE_CASE						,						param_name='''crop_size'''  )

           UpperCamelCase__    :   Any					=				make_list_of_images(__SCREAMING_SNAKE_CASE  )

           if not valid_images(__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

           if do_resize and size is None:
                 raise ValueError('''Size must be specified if do_resize is True.'''  )

           if do_center_crop and crop_size is None:
                 raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

           if do_rescale and rescale_factor is None:
                 raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

           # All transformations expect numpy arrays.
           UpperCamelCase__    :   int					=				[to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_resize:
                 UpperCamelCase__    :   Union[str, Any]					=				[self.resize(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_center_crop:
                 UpperCamelCase__    :   Any					=				[self.center_crop(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   Union[str, Any]					=				[self.rescale(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   int					=				[self.normalize(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   str					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE						,						tensor_type=__SCREAMING_SNAKE_CASE  )




from argparse import ArgumentParser

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


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

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




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				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:
                 UpperCamelCase__    :   Union[str, Any]					=				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  )


