
'''simple docstring'''

import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
     from cookiecutter.main import cookiecutter

     snake_case									=    True
except ImportError:
     snake_case									=    False

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


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return AddNewModelCommand(args.testing      ,			args.testing_file      ,			path=args.path			)







class    lowerCAmelCase       (     _a  ):


 @staticmethod
 def    _A	(    a__  : ArgumentParser    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	parser.add_parser("add-new-model"    )
  add_new_model_parser.add_argument("--testing"			,							action="store_true"			,							help="If in testing mode."    )
  add_new_model_parser.add_argument("--testing_file"			,							type=lowerCAmelCase_			,							help="Configuration file on which to run."    )
  add_new_model_parser.add_argument(
      "--path"			,							type=lowerCAmelCase_			,							help="Path to cookiecutter. Should only be used for testing purposes."    )
  add_new_model_parser.set_defaults(func=lowerCAmelCase_    )


 def __init__(    self  : Optional[Any]			,							a__  : bool			,							a__  : str			,							a__  : List[str]=None			,							*a__  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	testing
  lowerCAmelCase__     :   Tuple     		=	testing_file
  lowerCAmelCase__     :   Union[str, Any]     		=	path


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  warnings.warn(
      "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
      "It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality "
      "checks, you should use `transformers-cli add-new-model-like` instead."    )
  if not _has_cookiecutter:
   raise ImportError(
       "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
       "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"    )
  # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
  lowerCAmelCase__     :   List[str]     		=	[directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
  if len(lowerCAmelCase_    ) > 0:
   raise ValueError(
       "Several directories starting with `cookiecutter-template-` in current working directory. "
       "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
       "change your working directory."    )

  lowerCAmelCase__     :   Optional[int]     		=	(
      Path(lowerCAmelCase_    ).parent.parent.parent.parent if self._path is None else Path(self._path    ).parent.parent
  )
  lowerCAmelCase__     :   Optional[Any]     		=	path_to_transformer_root / "templates" / "adding_a_new_model"

  # Execute cookiecutter
  if not self._testing:
   cookiecutter(str(lowerCAmelCase_    )    )
  else:
   with open(self._testing_file			,							"r"    ) as configuration_file:
    lowerCAmelCase__     :   List[str]     		=	json.load(lowerCAmelCase_    )

   cookiecutter(
       str(path_to_cookiecutter if self._path is None else self._path    )			,							no_input=lowerCAmelCase_			,							extra_context=lowerCAmelCase_			,							)

  lowerCAmelCase__     :   Any     		=	[directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

  # Retrieve configuration
  with open(directory + "/configuration.json"			,							"r"    ) as configuration_file:
   lowerCAmelCase__     :   Optional[Any]     		=	json.load(lowerCAmelCase_    )

  lowerCAmelCase__     :   Union[str, Any]     		=	configuration["lowercase_modelname"]
  lowerCAmelCase__     :   Dict     		=	configuration["generate_tensorflow_pytorch_and_flax"]
  os.remove(F'''{directory}/configuration.json'''    )

  lowerCAmelCase__     :   str     		=	"PyTorch" in generate_tensorflow_pytorch_and_flax
  lowerCAmelCase__     :   Optional[Any]     		=	"TensorFlow" in generate_tensorflow_pytorch_and_flax
  lowerCAmelCase__     :   int     		=	"Flax" in generate_tensorflow_pytorch_and_flax

  lowerCAmelCase__     :   List[str]     		=	F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
  os.makedirs(lowerCAmelCase_			,							exist_ok=lowerCAmelCase_    )
  os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}'''			,							exist_ok=lowerCAmelCase_    )

  # Tests require submodules as they have parent imports
  with open(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'''			,							"w"    ):
   pass

  shutil.move(
      F'''{directory}/__init__.py'''			,							F'''{model_dir}/__init__.py'''			,							)
  shutil.move(
      F'''{directory}/configuration_{lowercase_model_name}.py'''			,							F'''{model_dir}/configuration_{lowercase_model_name}.py'''			,							)

  def remove_copy_lines(a__  : int    ):
   with open(lowerCAmelCase_			,							"r"    ) as f:
    lowerCAmelCase__     :   Optional[Any]     		=	f.readlines()
   with open(lowerCAmelCase_			,							"w"    ) as f:
    for line in lines:
     if "# Copied from transformers." not in line:
      f.write(lowerCAmelCase_    )

  if output_pytorch:
   if not self._testing:
    remove_copy_lines(F'''{directory}/modeling_{lowercase_model_name}.py'''    )

   shutil.move(
       F'''{directory}/modeling_{lowercase_model_name}.py'''			,							F'''{model_dir}/modeling_{lowercase_model_name}.py'''			,							)

   shutil.move(
       F'''{directory}/test_modeling_{lowercase_model_name}.py'''			,							F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'''			,							)
  else:
   os.remove(F'''{directory}/modeling_{lowercase_model_name}.py'''    )
   os.remove(F'''{directory}/test_modeling_{lowercase_model_name}.py'''    )

  if output_tensorflow:
   if not self._testing:
    remove_copy_lines(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''    )

   shutil.move(
       F'''{directory}/modeling_tf_{lowercase_model_name}.py'''			,							F'''{model_dir}/modeling_tf_{lowercase_model_name}.py'''			,							)

   shutil.move(
       F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''			,							F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'''			,							)
  else:
   os.remove(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''    )
   os.remove(F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''    )

  if output_flax:
   if not self._testing:
    remove_copy_lines(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''    )

   shutil.move(
       F'''{directory}/modeling_flax_{lowercase_model_name}.py'''			,							F'''{model_dir}/modeling_flax_{lowercase_model_name}.py'''			,							)

   shutil.move(
       F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''			,							F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'''			,							)
  else:
   os.remove(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''    )
   os.remove(F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''    )

  shutil.move(
      F'''{directory}/{lowercase_model_name}.md'''			,							F'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'''			,							)

  shutil.move(
      F'''{directory}/tokenization_{lowercase_model_name}.py'''			,							F'''{model_dir}/tokenization_{lowercase_model_name}.py'''			,							)

  shutil.move(
      F'''{directory}/tokenization_fast_{lowercase_model_name}.py'''			,							F'''{model_dir}/tokenization_{lowercase_model_name}_fast.py'''			,							)

  from os import fdopen, remove
  from shutil import copymode, move
  from tempfile import mkstemp

  def replace(a__  : str			,							a__  : str			,							a__  : List[str]    ):
   # Create temp file
   lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	mkstemp()
   lowerCAmelCase__     :   Dict     		=	False
   with fdopen(lowerCAmelCase_			,							"w"    ) as new_file:
    with open(lowerCAmelCase_    ) as old_file:
     for line in old_file:
      new_file.write(lowerCAmelCase_    )
      if line_to_copy_below in line:
       lowerCAmelCase__     :   int     		=	True
       for line_to_copy in lines_to_copy:
        new_file.write(lowerCAmelCase_    )

   if not line_found:
    raise ValueError(F'''Line {line_to_copy_below} was not found in file.'''    )

   # Copy the file permissions from the old file to the new file
   copymode(lowerCAmelCase_			,							lowerCAmelCase_    )
   # Remove original file
   remove(lowerCAmelCase_    )
   # Move new file
   move(lowerCAmelCase_			,							lowerCAmelCase_    )

  def skip_units(a__  : str    ):
   return (
       ("generating PyTorch" in line and not output_pytorch)
       or ("generating TensorFlow" in line and not output_tensorflow)
       or ("generating Flax" in line and not output_flax)
   )

  def replace_in_files(a__  : Optional[Any]    ):
   with open(lowerCAmelCase_    ) as datafile:
    lowerCAmelCase__     :   List[Any]     		=	[]
    lowerCAmelCase__     :   Optional[Any]     		=	False
    lowerCAmelCase__     :   List[str]     		=	False
    for line in datafile:
     if "# To replace in: " in line and "##" not in line:
      lowerCAmelCase__     :   Any     		=	line.split("\""    )[1]
      lowerCAmelCase__     :   Union[str, Any]     		=	skip_units(lowerCAmelCase_    )
     elif "# Below: " in line and "##" not in line:
      lowerCAmelCase__     :   Any     		=	line.split("\""    )[1]
      lowerCAmelCase__     :   Dict     		=	skip_units(lowerCAmelCase_    )
     elif "# End." in line and "##" not in line:
      if not skip_file and not skip_snippet:
       replace(lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_    )

      lowerCAmelCase__     :   Tuple     		=	[]
     elif "# Replace with" in line and "##" not in line:
      lowerCAmelCase__     :   Optional[int]     		=	[]
     elif "##" not in line:
      lines_to_copy.append(lowerCAmelCase_    )

   remove(lowerCAmelCase_    )

  replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''    )
  os.rmdir(lowerCAmelCase_    )











'''simple docstring'''


from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Tuple   )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							make_list_of_images(lowerCAmelCase_   )

														if not valid_images(lowerCAmelCase_   ):
																					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.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

														if do_resize:
																					_a      	=							[self.resize(image=lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_   ) for image in images]

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

														if do_rescale:
																					_a      	=							[self.rescale(image=lowerCAmelCase_				,     scale=lowerCAmelCase_   ) for image in images]

														if do_normalize:
																					_a      	=							[self.normalize(image=lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_   ) for image in images]

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

