
"""simple docstring"""


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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


_lowercase							   =				logging.get_logger(__name__)






def       _snake_case				(		snake_case__   :     np.ndarray	,      snake_case__   :     Union[int, Iterable[int]]	,      snake_case__   :     bool	,      snake_case__   :     int     ):
	def constraint_to_multiple_of(snake_case__   :     Dict	,      snake_case__   :     Optional[Any]	,      snake_case__   :     Union[str, Any]=0	,      snake_case__   :     List[Any]=None     ):
		A						   =   round(val / multiple     ) * multiple

		if max_val is not None and x > max_val:
			A						   =   math.floor(val / multiple     ) * multiple

		if x < min_val:
			A						   =   math.ceil(val / multiple     ) * multiple

		return x

	A						   =   (output_size, output_size) if isinstance(snake_case__	,      snake_case__     ) else output_size

	A   ,      A						   =   get_image_size(snake_case__     )
	A   ,      A						   =   output_size

	# determine new height and width
	A						   =   output_height / input_height
	A						   =   output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width     ) < abs(1 - scale_height     ):
			# fit width
			A						   =   scale_width
		else:
			# fit height
			A						   =   scale_height

	A						   =   constraint_to_multiple_of(scale_height * input_height	,      multiple=snake_case__     )
	A						   =   constraint_to_multiple_of(scale_width * input_width	,      multiple=snake_case__     )

	return (new_height, new_width)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = False  ,A_     :      int = 1  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Any  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'height': 384, 'width': 384}
							A						   =   get_size_dict(A_      )
							A						   =   do_resize
							A						   =   size
							A						   =   keep_aspect_ratio
							A						   =   ensure_multiple_of
							A						   =   resample
							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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      bool = False  ,A_     :      int = 1  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(
							    A_  ,output_size=(size['height'], size['width'])  ,keep_aspect_ratio=A_  ,multiple=A_  ,)
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      np.ndarray  ,A_     :      Union[int, float]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]  ,)     ->   Tuple:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Tuple  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      ImageInput  ,A_     :      bool = None  ,A_     :      int = None  ,A_     :      bool = None  ,A_     :      int = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      float = None  ,A_     :      bool = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      ChannelDimension = ChannelDimension.FIRST  ,**A_     :      Optional[Any]  ,)     ->   PIL.Image.Image:
							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(A_      )
							A						   =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							A						   =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							A						   =   resample if resample is not None else self.resample
							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(A_      )

							if not valid_images(A_      ):
								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_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(A_      ) for image in images]

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

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

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

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      List[Tuple] = None      )     ->   Tuple:
							A						   =   outputs.logits

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

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

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

							return semantic_segmentation







"""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

		_lowercase							   =				True
except ImportError:
		_lowercase							   =				False

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






def       _snake_case				(		snake_case__   :     Namespace     ):
	return AddNewModelCommand(args.testing	,      args.testing_file	,      path=args.path     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   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=A_  ,help='Configuration file on which to run.'      )
							add_new_model_parser.add_argument(
							    '--path'  ,type=A_  ,help='Path to cookiecutter. Should only be used for testing purposes.'      )
							add_new_model_parser.set_defaults(func=A_      )


						def __init__( self     :      Tuple  ,A_     :      bool  ,A_     :      str  ,A_     :      Tuple=None  ,*A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   testing
							A						   =   testing_file
							A						   =   path




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   int:
							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
							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
							if len(A_      ) > 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.'      )

							A						   =   (
							    Path(A_      ).parent.parent.parent.parent if self._path is None else Path(self._path      ).parent.parent
							)
							A						   =   path_to_transformer_root / 'templates' / 'adding_a_new_model'

							# Execute cookiecutter
							if not self._testing:
								cookiecutter(str(A_      )      )
							else:
								with open(self._testing_file  ,'r'      ) as configuration_file:
									A						   =   json.load(A_      )

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

							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

							# Retrieve configuration
							with open(directory + '/configuration.json'  ,'r'      ) as configuration_file:
								A						   =   json.load(A_      )

							A						   =   configuration['lowercase_modelname']
							A						   =   configuration['generate_tensorflow_pytorch_and_flax']
							os.remove(F'{directory}/configuration.json'      )

							A						   =   'PyTorch' in generate_tensorflow_pytorch_and_flax
							A						   =   'TensorFlow' in generate_tensorflow_pytorch_and_flax
							A						   =   'Flax' in generate_tensorflow_pytorch_and_flax

							A						   =   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
							os.makedirs(A_  ,exist_ok=A_      )
							os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'  ,exist_ok=A_      )

							# 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(A_  ,'r'      ) as f:
									A						   =   f.readlines()
								with open(A_  ,'w'      ) as f:
									for line in lines:
										if "# Copied from transformers." not in line:
											f.write(A_      )

							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
								A   ,      A						   =   mkstemp()
								A						   =   False
								with fdopen(A_  ,'w'      ) as new_file:
									with open(A_      ) as old_file:
										for line in old_file:
											new_file.write(A_      )
											if line_to_copy_below in line:
												A						   =   True
												for line_to_copy in lines_to_copy:
													new_file.write(A_      )

								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(A_  ,A_      )
								# Remove original file
								remove(A_      )
								# Move new file
								move(A_  ,A_      )

							def skip_units(A_     :      Dict      ):
								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_     :      Tuple      ):
								with open(A_      ) as datafile:
									A						   =   []
									A						   =   False
									A						   =   False
									for line in datafile:
										if "# To replace in: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# Below: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# End." in line and "##" not in line:
											if not skip_file and not skip_snippet:
												replace(A_  ,A_  ,A_      )

											A						   =   []
										elif "# Replace with" in line and "##" not in line:
											A						   =   []
										elif "##" not in line:
											lines_to_copy.append(A_      )

								remove(A_      )

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