




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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


lowerCamelCase	:	Any				=   logging.get_logger(__name__)




class 						__lowercase		(UpperCamelCase__							):




						"""simple docstring"""





						_snake_case												=						["""pixel_values"""]

						def __init__(				self    ,				A = True    ,				A = None    ,				A = PILImageResampling.BILINEAR    ,				A = True    ,				A = 1 / 2_5_5    ,				A = True    ,				A = None    ,				A = True    ,				**A    ,				)		->    None:
									super().__init__(**A     )
									snake_case				:    Optional[Any]           =	size if size is not None else {"""shortest_edge""": 2_2_4}
									snake_case				:    Optional[Any]           =	get_size_dict(A    ,				default_to_square=A     )
									snake_case				:    Tuple           =	crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
									snake_case				:    Any           =	get_size_dict(A    ,				param_name="""crop_size"""     )

									snake_case				:    int           =	do_resize
									snake_case				:    Tuple           =	size
									snake_case				:    Optional[int]           =	resample
									snake_case				:    Tuple           =	do_rescale
									snake_case				:    Optional[int]           =	rescale_factor
									snake_case				:    str           =	do_center_crop
									snake_case				:    Union[str, Any]           =	crop_size
									snake_case				:    Tuple           =	do_flip_channel_order

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = PIL.Image.BILINEAR    ,				A = None    ,				**A    ,				)		->    np.ndarray:
									snake_case				:    Union[str, Any]           =	get_size_dict(A    ,				default_to_square=A     )
									if "shortest_edge" not in size:
												raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""     )
									snake_case				:    Any           =	get_resize_output_image_size(A    ,				size=size["""shortest_edge"""]    ,				default_to_square=A     )
									return resize(A    ,				size=A    ,				resample=A    ,				data_format=A    ,				**A     )

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    np.ndarray:
									snake_case				:    Optional[Any]           =	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()}"""     )
									return center_crop(A    ,				size=(size["""height"""], size["""width"""])    ,				data_format=A    ,				**A     )

						def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A = None    ,				**A    ,				)		->    str:
									return rescale(A    ,				scale=A    ,				data_format=A    ,				**A     )

						def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    np.ndarray:
									return flip_channel_order(A    ,				data_format=A     )

						def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = ChannelDimension.FIRST    ,				**A    ,				)		->    PIL.Image.Image:
									snake_case				:    Dict           =	do_resize if do_resize is not None else self.do_resize
									snake_case				:    Optional[Any]           =	resample if resample is not None else self.resample
									snake_case				:    Union[str, Any]           =	do_rescale if do_rescale is not None else self.do_rescale
									snake_case				:    int           =	rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case				:    Optional[int]           =	do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case				:    int           =	(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									snake_case				:    List[str]           =	size if size is not None else self.size
									snake_case				:    List[Any]           =	get_size_dict(A    ,				default_to_square=A     )
									snake_case				:    int           =	crop_size if crop_size is not None else self.crop_size
									snake_case				:    Union[str, Any]           =	get_size_dict(A    ,				param_name="""crop_size"""     )

									snake_case				:    Dict           =	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:
												raise ValueError("""Size 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_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""     )

									# All transformations expect numpy arrays.
									snake_case				:    List[Any]           =	[to_numpy_array(A     ) for image in images]

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

									if do_center_crop:
												snake_case				:    List[str]           =	[self.center_crop(image=A    ,				size=A     ) for image in images]

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

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
												snake_case				:    int           =	[self.flip_channel_order(image=A     ) for image in images]

									snake_case				:    Optional[int]           =	[to_channel_dimension_format(A    ,				A     ) for image in images]

									snake_case				:    Optional[int]           =	{"""pixel_values""": images}
									return BatchFeature(data=A    ,				tensor_type=A     )

						def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Optional[int]:
									snake_case				:    str           =	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     ):
															snake_case				:    Tuple           =	target_sizes.numpy()

												snake_case				:    str           =	[]

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

									return semantic_segmentation











from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   return ConvertCommand(
       args.model_type						,args.tf_checkpoint						,args.pytorch_dump_output						,args.config						,args.finetuning_task_name					)


lowerCamelCase	:	Tuple				=   '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      @staticmethod
      def      UpperCAmelCase				(				A     )		->    List[str]:
         snake_case				:    Union[str, Any]           =	parser.add_parser(
             """convert"""    ,				help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""    ,				)
         train_parser.add_argument("""--model_type"""    ,				type=A    ,				required=A    ,				help="""Model's type."""     )
         train_parser.add_argument(
             """--tf_checkpoint"""    ,				type=A    ,				required=A    ,				help="""TensorFlow checkpoint path or folder."""     )
         train_parser.add_argument(
             """--pytorch_dump_output"""    ,				type=A    ,				required=A    ,				help="""Path to the PyTorch saved model output."""     )
         train_parser.add_argument("""--config"""    ,				type=A    ,				default=""""""    ,				help="""Configuration file path or folder."""     )
         train_parser.add_argument(
             """--finetuning_task_name"""    ,				type=A    ,				default=A    ,				help="""Optional fine-tuning task name if the TF model was a finetuned model."""    ,				)
         train_parser.set_defaults(func=A     )

      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				*A    ,				)		->    List[Any]:
         snake_case				:    Any           =	logging.get_logger("""transformers-cli/converting"""     )

         self._logger.info(f"""Loading model {model_type}"""     )
         snake_case				:    int           =	model_type
         snake_case				:    Any           =	tf_checkpoint
         snake_case				:    int           =	pytorch_dump_output
         snake_case				:    List[str]           =	config
         snake_case				:    Tuple           =	finetuning_task_name

      def      UpperCAmelCase				(				self     )		->    int:
         if self._model_type == "albert":
            try:
               from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "bert":
            try:
               from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "funnel":
            try:
               from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "t5":
            try:
               from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "gpt":
            from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                convert_openai_checkpoint_to_pytorch,
            )

            convert_openai_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "transfo_xl":
            try:
               from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                   convert_transfo_xl_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            if "ckpt" in self._tf_checkpoint.lower():
               snake_case				:    List[Any]           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            else:
               snake_case				:    Tuple           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            convert_transfo_xl_checkpoint_to_pytorch(
                A    ,				self._config    ,				self._pytorch_dump_output    ,				A     )
         elif self._model_type == "gpt2":
            try:
               from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                   convert_gpta_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "xlnet":
            try:
               from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                   convert_xlnet_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_xlnet_checkpoint_to_pytorch(
                self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output    ,				self._finetuning_task_name     )
         elif self._model_type == "xlm":
            from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                convert_xlm_checkpoint_to_pytorch,
            )

            convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "lxmert":
            from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                convert_lxmert_checkpoint_to_pytorch,
            )

            convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "rembert":
            from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                convert_rembert_tf_checkpoint_to_pytorch,
            )

            convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         else:
            raise ValueError(
                """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""     )





