





"""simple docstring"""




from typing import Dict, 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, is_vision_available, logging


if is_vision_available():
   import PIL


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Optional[int]	    =   ["""pixel_values"""]

      def __init__(     self					,      snake_case_ = True					,      snake_case_ = None					,      snake_case_ = 0.9					,      snake_case_ = PILImageResampling.BICUBIC					,      snake_case_ = True					,      snake_case_ = None					,      snake_case_ = 1 / 2_5_5					,      snake_case_ = True					,      snake_case_ = True					,      snake_case_ = None					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

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

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      snake_case_ = PILImageResampling.BICUBIC					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       Optional[int]										=  get_size_dict(snake_case_					,      default_to_square=snake_case_  )
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
          raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
        if crop_pct is not None:
          if "shortest_edge" in size:
            A_  :       int										=  int(size['shortest_edge'] / crop_pct  )
          elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
              A_  :       List[Any]										=  int(size['height'] / crop_pct  )
            else:
              A_  :       str										=  (int(size['height'] / crop_pct  ), int(size['width'] / crop_pct  ))
          else:
            raise ValueError('Invalid size for resize: {}'.format(snake_case_  )  )

          A_  :       Union[str, Any]										=  get_resize_output_image_size(snake_case_					,      size=snake_case_					,      default_to_square=snake_case_  )
        else:
          if "shortest_edge" in size:
            A_  :       Optional[Any]										=  get_resize_output_image_size(snake_case_					,      size=size['shortest_edge']					,      default_to_square=snake_case_  )
          elif "height" in size and "width" in size:
            A_  :       Dict										=  (size['height'], size['width'])
          else:
            raise ValueError('Invalid size for resize: {}'.format(snake_case_  )  )

        return resize(snake_case_					,      size=snake_case_					,      resample=snake_case_					,      data_format=snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  get_size_dict(snake_case_  )
        if "height" not in size or "width" not in size:
          raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""  )
        return center_crop(snake_case_					,      size=(size['height'], size['width'])					,      data_format=snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = ChannelDimension.FIRST					,      **snake_case_					,      ):

        """simple docstring"""

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

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

        A_  :       Optional[Any]										=  make_list_of_images(snake_case_  )

        if not valid_images(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 or resample is None:
          raise ValueError('Size and resample must be specified if do_resize is True.'  )

        if do_center_crop and crop_pct is None:
          raise ValueError('Crop_pct 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.
        A_  :       int										=  [to_numpy_array(snake_case_  ) for image in images]

        if do_resize:
          A_  :       Optional[Any]										=  [self.resize(image=snake_case_					,      size=snake_case_					,      crop_pct=snake_case_					,      resample=snake_case_  ) for image in images]

        if do_center_crop:
          A_  :       Any										=  [self.center_crop(image=snake_case_					,      size=snake_case_  ) for image in images]

        if do_rescale:
          A_  :       Union[str, Any]										=  [self.rescale(image=snake_case_					,      scale=snake_case_  ) for image in images]

        if do_normalize:
          A_  :       Any										=  [self.normalize(image=snake_case_					,      mean=snake_case_					,      std=snake_case_  ) for image in images]

        A_  :       str										=  [to_channel_dimension_format(snake_case_					,      snake_case_  ) for image in images]

        A_  :       List[str]										=  {'pixel_values': images}
        return BatchFeature(data=snake_case_					,      tensor_type=snake_case_  )






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)