
"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase_					:						Any				= logging.get_logger(__name__)

UpperCamelCase_					:						List[Any]				= {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class __lowerCAmelCase				(					_lowercase     ,				_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"swin"

			snake_case	      =				{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}




			def __init__(				self						:					List[Any]       ,   _snake_case						:					Any=224       ,   _snake_case						:					Optional[int]=4       ,   _snake_case						:					str=3       ,   _snake_case						:					Any=96       ,   _snake_case						:					List[Any]=[2, 2, 6, 2]       ,   _snake_case						:					List[str]=[3, 6, 12, 24]       ,   _snake_case						:					Dict=7       ,   _snake_case						:					List[Any]=4.0       ,   _snake_case						:					Any=True       ,   _snake_case						:					Optional[Any]=0.0       ,   _snake_case						:					List[str]=0.0       ,   _snake_case						:					Union[str, Any]=0.1       ,   _snake_case						:					Dict="gelu"       ,   _snake_case						:					Optional[int]=False       ,   _snake_case						:					int=0.0_2       ,   _snake_case						:					Any=1e-5       ,   _snake_case						:					List[str]=32       ,   _snake_case						:					Optional[int]=None       ,   _snake_case						:					Optional[Any]=None       ,   **_snake_case						:					Optional[Any]       ,   )      -> List[str]:







								"""simple docstring"""
								super().__init__(**_snake_case	)

								A_				 =						image_size
								A_				 =						patch_size
								A_				 =						num_channels
								A_				 =						embed_dim
								A_				 =						depths
								A_				 =						len(_snake_case	)
								A_				 =						num_heads
								A_				 =						window_size
								A_				 =						mlp_ratio
								A_				 =						qkv_bias
								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						drop_path_rate
								A_				 =						hidden_act
								A_				 =						use_absolute_embeddings
								A_				 =						layer_norm_eps
								A_				 =						initializer_range
								A_				 =						encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								A_				 =						int(embed_dim * 2 ** (len(_snake_case	) - 1)	)
								A_				 =						["stem"] + [F'stage{idx}' for idx in range(1       ,   len(_snake_case	) + 1	)]
								A_ ,    A_				 =						get_aligned_output_features_output_indices(
								    out_features=_snake_case       ,   out_indices=_snake_case       ,   stage_names=self.stage_names	)





class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				version.parse("1.11"      )




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> Mapping[str, Mapping[int, str]]:







								"""simple docstring"""
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]	)






			@property
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> float:







								"""simple docstring"""
								return 1e-4






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





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


UpperCamelCase_					:						Tuple				= '''
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 __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







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




			def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> 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      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""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(_snake_case	)

													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(_snake_case	)

													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(_snake_case	)

													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(_snake_case	)

													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(_snake_case	)

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

													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(_snake_case	)

													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]"	)




