

'''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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					_UpperCamelCase			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = True      ,      lowercase = True      ,      lowercase = 1 / 255      ,      lowercase = None      ,      lowercase = True      ,      lowercase = None      ,      lowercase = None      ,      **lowercase      ,      )  ->						int:
							'''simple docstring'''






							super().__init__(**_UpperCAmelCase       )
							A__        =			size if size is not None else {'''height''': 224, '''width''': 224}
							A__        =			get_size_dict(_UpperCAmelCase       )
							A__        =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							A__        =			get_size_dict(_UpperCAmelCase      ,      default_to_square=_UpperCAmelCase      ,      param_name="crop_size"       )

							A__        =			do_resize
							A__        =			do_rescale
							A__        =			do_normalize
							A__        =			do_center_crop
							A__        =			crop_size
							A__        =			size
							A__        =			resample
							A__        =			rescale_factor
							A__        =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							A__        =			image_std if image_std is not None else IMAGENET_DEFAULT_STD


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = None      ,      **lowercase      ,      )  ->						List[str]:
							'''simple docstring'''






							A__        =			get_size_dict(_UpperCAmelCase       )

							if "shortest_edge" in size:
											A__        =			get_resize_output_image_size(_UpperCAmelCase      ,      size=size["shortest_edge"]      ,      default_to_square=_UpperCAmelCase       )
											# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
											A__        =			(size['''height'''], size['''width'''])
							else:
											raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'       )
							return resize(_UpperCAmelCase      ,      size=_UpperCAmelCase      ,      resample=_UpperCAmelCase      ,      data_format=_UpperCAmelCase      ,      **_UpperCAmelCase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase       )  ->						int:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						Dict:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = ChannelDimension.FIRST      ,      **lowercase      ,      )  ->						str:
							'''simple docstring'''






							A__        =			do_resize if do_resize is not None else self.do_resize
							A__        =			do_rescale if do_rescale is not None else self.do_rescale
							A__        =			do_normalize if do_normalize is not None else self.do_normalize
							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(_UpperCAmelCase      ,      param_name="crop_size"      ,      default_to_square=_UpperCAmelCase       )
							A__        =			resample if resample is not None else self.resample
							A__        =			rescale_factor if rescale_factor is not None else self.rescale_factor
							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__        =			size if size is not None else self.size
							A__        =			get_size_dict(_UpperCAmelCase       )

							if not is_batched(_UpperCAmelCase       ):
											A__        =			[images]

							if not valid_images(_UpperCAmelCase       ):
											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."       )

							# All transformations expect numpy arrays.
							A__        =			[to_numpy_array(_UpperCAmelCase       ) for image in images]

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

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

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

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

							A__        =			[to_channel_dimension_format(_UpperCAmelCase      ,      _UpperCAmelCase       ) for image in images]

							A__        =			{'''pixel_values''': images}
							return BatchFeature(data=_UpperCAmelCase      ,      tensor_type=_UpperCAmelCase       )




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Namespace      )			->      Tuple:




				'''simple docstring'''





				return ConvertCommand(
				    args.model_type	,       args.tf_checkpoint	,       args.pytorch_dump_output	,       args.config	,       args.finetuning_task_name      )


lowerCAmelCase__	   = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class 					a__						(					snake_case			):
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			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=lowercase      ,      required=lowercase      ,      help="Model's type."       )
							train_parser.add_argument(
							    "--tf_checkpoint"      ,      type=lowercase      ,      required=lowercase      ,      help="TensorFlow checkpoint path or folder."       )
							train_parser.add_argument(
							    "--pytorch_dump_output"      ,      type=lowercase      ,      required=lowercase      ,      help="Path to the PyTorch saved model output."       )
							train_parser.add_argument("--config"      ,      type=lowercase      ,      default=""      ,      help="Configuration file path or folder."       )
							train_parser.add_argument(
							    "--finetuning_task_name"      ,      type=lowercase      ,      default=lowercase      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
							train_parser.set_defaults(func=lowercase       )


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      *lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			logging.get_logger("transformers-cli/converting"       )

							self._logger.info(F'Loading model {model_type}'       )
							A__        =			model_type
							A__        =			tf_checkpoint
							A__        =			pytorch_dump_output
							A__        =			config
							A__        =			finetuning_task_name


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							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(lowercase       )

											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(lowercase       )

											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(lowercase       )

											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(lowercase       )

											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(lowercase       )

											if "ckpt" in self._tf_checkpoint.lower():
															A__        =			self._tf_checkpoint
															A__        =			""
											else:
															A__        =			self._tf_checkpoint
															A__        =			""
											convert_transfo_xl_checkpoint_to_pytorch(
											    lowercase      ,      self._config      ,      self._pytorch_dump_output      ,      lowercase       )
							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(lowercase       )

											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(lowercase       )

											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]"       )

