

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      SwinConfig(image_size=1_9_2)

			if "base" in model_name:
						a_		:		Dict      				=      6
						a_		:		Union[str, Any]      				=      1_2_8
						a_		:		Optional[Any]      				=      (2, 2, 1_8, 2)
						a_		:		Optional[Any]      				=      (4, 8, 1_6, 3_2)
			elif "large" in model_name:
						a_		:		Union[str, Any]      				=      1_2
						a_		:		Dict      				=      1_9_2
						a_		:		Tuple      				=      (2, 2, 1_8, 2)
						a_		:		int      				=      (6, 1_2, 2_4, 4_8)
			else:
						raise ValueError("""Model not supported, only supports base and large variants""")

			a_		:		Dict      				=      window_size
			a_		:		Any      				=      embed_dim
			a_		:		Union[str, Any]      				=      depths
			a_		:		List[str]      				=      num_heads

			return config


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			if "encoder.mask_token" in name:
						a_		:		Dict      				=      name.replace("""encoder.mask_token"""   ,     """embeddings.mask_token""")
			if "encoder.patch_embed.proj" in name:
						a_		:		Optional[int]      				=      name.replace("""encoder.patch_embed.proj"""   ,     """embeddings.patch_embeddings.projection""")
			if "encoder.patch_embed.norm" in name:
						a_		:		Optional[int]      				=      name.replace("""encoder.patch_embed.norm"""   ,     """embeddings.norm""")
			if "attn.proj" in name:
						a_		:		Union[str, Any]      				=      name.replace("""attn.proj"""   ,     """attention.output.dense""")
			if "attn" in name:
						a_		:		Any      				=      name.replace("""attn"""   ,     """attention.self""")
			if "norm1" in name:
						a_		:		int      				=      name.replace("""norm1"""   ,     """layernorm_before""")
			if "norm2" in name:
						a_		:		Optional[Any]      				=      name.replace("""norm2"""   ,     """layernorm_after""")
			if "mlp.fc1" in name:
						a_		:		List[Any]      				=      name.replace("""mlp.fc1"""   ,     """intermediate.dense""")
			if "mlp.fc2" in name:
						a_		:		Tuple      				=      name.replace("""mlp.fc2"""   ,     """output.dense""")

			if name == "encoder.norm.weight":
						a_		:		Tuple      				=      """layernorm.weight"""
			if name == "encoder.norm.bias":
						a_		:		Tuple      				=      """layernorm.bias"""

			if "decoder" in name:
						pass
			else:
						a_		:		Any      				=      """swin.""" + name

			return name


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						a_		:		List[str]      				=      orig_state_dict.pop(a__)

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									a_		:		Union[str, Any]      				=      key.split(""".""")
									a_		:		str      				=      int(key_split[2])
									a_		:		Dict      				=      int(key_split[4])
									a_		:		Optional[int]      				=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												a_		:		Tuple      				=      val[:dim, :]
												a_		:		Tuple      				=      val[
												    dim : dim * 2, :
												]
												a_		:		Any      				=      val[-dim:, :]
									else:
												a_		:		str      				=      val[
												    :dim
												]
												a_		:		Optional[Any]      				=      val[
												    dim : dim * 2
												]
												a_		:		Dict      				=      val[
												    -dim:
												]
						else:
									a_		:		Optional[int]      				=      val

			return orig_state_dict


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Optional[Any]      				=      torch.load(a__   ,     map_location="""cpu""")["""model"""]

			a_		:		List[str]      				=      get_swin_config(a__)
			a_		:		Optional[int]      				=      SwinForMaskedImageModeling(a__)
			model.eval()

			a_		:		Any      				=      convert_state_dict(a__   ,     a__)
			model.load_state_dict(a__)

			a_		:		int      				=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

			a_		:		List[Any]      				=      ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2})
			a_		:		Tuple      				=      Image.open(requests.get(a__   ,     stream=a__).raw)
			a_		:		Optional[Any]      				=      image_processor(images=a__   ,     return_tensors="""pt""")

			with torch.no_grad():
						a_		:		List[Any]      				=      model(**a__).logits

			print(outputs.keys())
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)

						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						print(f'''Pushing model and image processor for {model_name} to hub''')
						model.push_to_hub(f'''microsoft/{model_name}''')
						image_processor.push_to_hub(f'''microsoft/{model_name}''')


if __name__ == "__main__":
	__snake_case							:	str											=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""swin-base-simmim-window6-192""",
	    type=str,
	    choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
	    help="""Name of the Swin SimMIM model you'd like to convert.""",
	)
	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
	    type=str,
	    help="""Path to the original PyTorch checkpoint (.pth file).""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	__snake_case							:	Optional[int]											=       parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowercase: Union[str, Any]      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																raise ValueError(
																    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
																    " please use only one"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)

						if prompt is not None:
											if not isinstance(lowerCamelCase_		,       lowerCamelCase_		):
																raise ValueError(
																    F'''Received an invalid text input, got - {type(lowerCamelCase_		)} - but expected a single string. '''
																    "Note also that one single text can be provided for conditional image to text generation."		)

											a					     =   self.model.config.model_type

											if model_type == "git":
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(text=lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		).input_ids
																a					     =   [self.tokenizer.cls_token_id] + input_ids
																a					     =   torch.tensor(lowerCamelCase_		).unsqueeze(0		)
																model_inputs.update({"input_ids": input_ids}		)

											elif model_type == "pix2struct":
																a					     =   self.image_processor(images=lowerCamelCase_		,       header_text=lowerCamelCase_		,       return_tensors=self.framework		)

											elif model_type != "vision-encoder-decoder":
																# vision-encoder-decoder does not support conditional generation
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(lowerCamelCase_		,       return_tensors=self.framework		)
																model_inputs.update(lowerCamelCase_		)

											else:
																raise ValueError(F'''Model type {model_type} does not support conditional text generation'''		)

						else:
											a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
											a					     =   None

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs["input_ids"]		,       lowerCamelCase_		)
						    and all(x is None for x in model_inputs["input_ids"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   []
						for output_ids in model_outputs:
											a					     =   {
											    "generated_text": self.tokenizer.decode(
											        lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       )
											}
											records.append(lowerCamelCase_		)
						return records
