




'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]      ):
  __a								=       SwinConfig(image_size=192      )

  if "base" in model_name:
    __a								=       6
    __a								=       128
    __a								=       (2, 2, 18, 2)
    __a								=       (4, 8, 16, 32)
  elif "large" in model_name:
    __a								=       12
    __a								=       192
    __a								=       (2, 2, 18, 2)
    __a								=       (6, 12, 24, 48)
  else:
    raise ValueError('Model not supported, only supports base and large variants'      )

  __a								=       window_size
  __a								=       embed_dim
  __a								=       depths
  __a								=       num_heads

  return config


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):

  if "encoder.mask_token" in name:
    __a								=       name.replace('encoder.mask_token'					,       'embeddings.mask_token'      )
  if "encoder.patch_embed.proj" in name:
    __a								=       name.replace('encoder.patch_embed.proj'					,       'embeddings.patch_embeddings.projection'      )
  if "encoder.patch_embed.norm" in name:
    __a								=       name.replace('encoder.patch_embed.norm'					,       'embeddings.norm'      )
  if "attn.proj" in name:
    __a								=       name.replace('attn.proj'					,       'attention.output.dense'      )
  if "attn" in name:
    __a								=       name.replace('attn'					,       'attention.self'      )
  if "norm1" in name:
    __a								=       name.replace('norm1'					,       'layernorm_before'      )
  if "norm2" in name:
    __a								=       name.replace('norm2'					,       'layernorm_after'      )
  if "mlp.fc1" in name:
    __a								=       name.replace('mlp.fc1'					,       'intermediate.dense'      )
  if "mlp.fc2" in name:
    __a								=       name.replace('mlp.fc2'					,       'output.dense'      )

  if name == "encoder.norm.weight":
    __a								=       'layernorm.weight'
  if name == "encoder.norm.bias":
    __a								=       'layernorm.bias'

  if "decoder" in name:
    pass
  else:
    __a								=       'swin.' + name

  return name


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				Any      ):

  for key in orig_state_dict.copy().keys():
    __a								=       orig_state_dict.pop(a_      )

    if "attn_mask" in key:
      pass
    elif "qkv" in key:
      __a								=       key.split('.'      )
      __a								=       int(key_split[2]      )
      __a								=       int(key_split[4]      )
      __a								=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __a								=       val[:dim, :]
        __a								=       val[
            dim : dim * 2, :
        ]
        __a								=       val[-dim:, :]
      else:
        __a								=       val[
            :dim
        ]
        __a								=       val[
            dim : dim * 2
        ]
        __a								=       val[
            -dim:
        ]
    else:
      __a								=       val

  return orig_state_dict


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				Union[str, Any]					,       a_						:				Union[str, Any]					,       a_						:				Any      ):
  __a								=       torch.load(a_					,       map_location='cpu'      )['model']

  __a								=       get_swin_config(a_      )
  __a								=       SwinForMaskedImageModeling(a_      )
  model.eval()

  __a								=       convert_state_dict(a_					,       a_      )
  model.load_state_dict(a_      )

  __a								=       'http://images.cocodataset.org/val2017/000000039769.jpg'

  __a								=       ViTImageProcessor(size={'height': 192, 'width': 192}      )
  __a								=       Image.open(requests.get(a_					,       stream=a_      ).raw      )
  __a								=       image_processor(images=a_					,       return_tensors='pt'      )

  with torch.no_grad():
    __a								=       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__":
  UpperCAmelCase_					=   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."
  )

  UpperCAmelCase_					=   parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
    from transformers import (
        FlaxBertModel,
        FlaxCLIPVisionModel,
        FlaxVisionTextDualEncoderModel,
        FlaxViTModel,
        VisionTextDualEncoderConfig,
        VisionTextDualEncoderProcessor,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


if is_torch_available():
    import torch

    from transformers import VisionTextDualEncoderModel

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if isinstance(A				,							collections.abc.Iterable     ):
      return x
   return (x, x)

@require_flax
class     snake_case_		:






 '''simple docstring'''





 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Dict:
    pass

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    pass

 def  __UpperCAmelCase						(  self		)	->       str:
    pass

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=np.abs((a - b)		).max()
    self.assertLessEqual(A_,		A_,		f"""Difference between torch and flax is {diff} (>= {tol})."""		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[str]:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], model.config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], model.config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       Any:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
    UpperCAmelCase__						=output[0]

    with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
       UpperCAmelCase__						=after_output[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-3		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(
        input_ids=A_,		pixel_values=A_,		attention_mask=A_,		output_attentions=A_		)

    UpperCAmelCase__						=output.vision_model_output.attentions
    self.assertEqual(len(A_		),		vision_config.num_hidden_layers		)

    # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase__						=to_atuple(vision_model.config.image_size		)
    UpperCAmelCase__						=to_atuple(vision_model.config.patch_size		)
    UpperCAmelCase__						=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    UpperCAmelCase__						=num_patches + 1
    self.assertEqual(vision_attentions[0].shape[-3:],		(vision_config.num_attention_heads, seq_len, seq_len)		)

    UpperCAmelCase__						=output.text_model_output.attentions
    self.assertEqual(len(A_		),		text_config.num_hidden_layers		)

    self.assertEqual(
        text_attentions[0].shape[-3:],		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Union[str, Any]:
    pt_model.to(A_		)
    pt_model.eval()

    # prepare inputs
    UpperCAmelCase__						=inputs_dict
    UpperCAmelCase__						={k: torch.tensor(v.tolist()		) for k, v in flax_inputs.items()}

    with torch.no_grad():
       UpperCAmelCase__						=pt_model(**A_		).to_tuple()

    UpperCAmelCase__						=fx_model(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output in zip(fx_outputs[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # PT -> Flax
    with tempfile.TemporaryDirectory() as tmpdirname:
       pt_model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_,		from_pt=A_		)

    UpperCAmelCase__						=fx_model_loaded(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # Flax -> PT
    with tempfile.TemporaryDirectory() as tmpdirname:
       fx_model.save_pretrained(A_		)
       UpperCAmelCase__						=VisionTextDualEncoderModel.from_pretrained(A_,		from_flax=A_		)

    pt_model_loaded.to(A_		)
    pt_model_loaded.eval()

    with torch.no_grad():
       UpperCAmelCase__						=pt_model_loaded(**A_		).to_tuple()

    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output_loaded in zip(fx_outputs[:4],		pt_outputs_loaded[:4]		):
       self.assert_almost_equals(A_,		pt_output_loaded.numpy(),		4E-2		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		A_		)
    UpperCAmelCase__						=fx_state

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Dict:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=load_flax_weights_in_pytorch_model(A_,		fx_model.params		)

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**A_		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_save_load(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**A_		)

 @is_pt_flax_cross_test
 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    UpperCAmelCase__						=config_inputs_dict.pop("vision_config"		)
    UpperCAmelCase__						=config_inputs_dict.pop("text_config"		)

    UpperCAmelCase__						=config_inputs_dict

    self.check_equivalence_pt_to_flax(A_,		A_,		A_		)
    self.check_equivalence_flax_to_pt(A_,		A_,		A_		)






 @slow
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_pretrained_model_and_inputs()

    UpperCAmelCase__						=model_a(**A_		)
    UpperCAmelCase__						=outputs[0]

    with tempfile.TemporaryDirectory() as tmp_dirname:
       model_a.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model_a(**A_		)
       UpperCAmelCase__						=after_outputs[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-5		)

@require_flax
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-vit",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=FlaxViTModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=FlaxViTModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=vit_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_torch
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-clip",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=FlaxCLIPVisionModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxCLIPVisionModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=clip_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_flax
@require_vision
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 @slow
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian",		logit_scale_init_value=1.0		)
    UpperCAmelCase__						=VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

    UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    UpperCAmelCase__						=processor(
        text=["una foto di un gatto", "una foto di un cane"],		images=A_,		padding=A_,		return_tensors="np"		)

    UpperCAmelCase__						=model(**A_		)

    # verify the logits
    self.assertEqual(outputs.logits_per_image.shape,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
    self.assertEqual(
        outputs.logits_per_text.shape,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),		)

    UpperCAmelCase__						=np.array([[1.2_28_47_27, 0.3_10_41_22]]		)

    self.assertTrue(np.allclose(outputs.logits_per_image,		A_,		atol=1E-3		)		)



