
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Union[str, Any]=				IFInpaintingSuperResolutionPipeline
    _a		:						Tuple=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
    _a		:						str=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}     )
    _a		:						Tuple=				PipelineTesterMixin.required_optional_params - {"latents"}
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._get_superresolution_dummy_components()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=0      ):



           '''simple docstring'''







           if str(snake_case      ).startswith("""mps"""      ):
                  lowercase							:     int       	=      torch.manual_seed(snake_case      )
           else:
                  lowercase							:     List[str]       	=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )

           lowercase							:     str       	=      floats_tensor((1, 3, 16, 16)		,rng=random.Random(snake_case      )      ).to(snake_case      )
           lowercase							:     Union[str, Any]       	=      floats_tensor((1, 3, 32, 32)		,rng=random.Random(snake_case      )      ).to(snake_case      )
           lowercase							:     str       	=      floats_tensor((1, 3, 32, 32)		,rng=random.Random(snake_case      )      ).to(snake_case      )

           lowercase							:     int       	=      {
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """original_image""": original_image,
               """mask_image""": mask_image,
               """generator""": generator,
               """num_inference_steps""": 2,
               """output_type""": """numpy""",
           }

           return inputs
    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()		,reason="""XFormers attention is only available with CUDA and `xformers` installed"""		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != """cuda"""		,reason="""float16 requires CUDA"""      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().test_save_load_floataa(expected_max_diff=1e-1      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_save_load_local()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2		,)


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
               f"stage{idx}.patch_embed.proj.weight",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
               f"stage{idx}.patch_embed.proj.bias",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
               f"stage{idx}.patch_embed.norm.weight",
           )				)
       embed.append(
           (
               f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
               f"stage{idx}.patch_embed.norm.bias",
           )				)
       return embed







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
               f"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
               f"stage{idx}.blocks.{cnt}.attn.proj.weight",
           )				)
       attention_weights.append(
           (
               f"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
               f"stage{idx}.blocks.{cnt}.attn.proj.bias",
           )				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc1.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc1.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", f"stage{idx}.blocks.{cnt}.mlp.fc2.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", f"stage{idx}.blocks.{cnt}.mlp.fc2.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", f"stage{idx}.blocks.{cnt}.norm1.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", f"stage{idx}.blocks.{cnt}.norm1.bias")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", f"stage{idx}.blocks.{cnt}.norm2.weight")				)
       attention_weights.append(
           (f"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", f"stage{idx}.blocks.{cnt}.norm2.bias")				)
       return attention_weights







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[Any]       	=      []
       head.append(("""layernorm.weight""", """norm.weight""")				)
       head.append(("""layernorm.bias""", """norm.bias""")				)
       head.append(("""classifier.weight""", """head.weight""")				)
       head.append(("""classifier.bias""", """head.bias""")				)
       return head







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

       lowercase							:     int       	=      """huggingface/label-files"""
       lowercase							:     Union[str, Any]       	=      num_labels
       lowercase							:     Optional[Any]       	=      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""				)				)					,	"""r"""				)				)
       lowercase							:     List[Any]       	=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

       lowercase							:     Dict       	=      idalabel
       lowercase							:     List[str]       	=      {v: k for k, v in idalabel.items()}

       lowercase							:     List[str]       	=      CvtConfig(num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__				)

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit("""/"""					,	1				)[-1][4:6] == "13":
              lowercase							:     Tuple       	=      [1, 2, 10]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit("""/"""					,	1				)[-1][4:6] == "21":
              lowercase							:     Dict       	=      [1, 4, 16]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              lowercase							:     int       	=      [2, 2, 20]
              lowercase							:     Optional[int]       	=      [3, 12, 16]
              lowercase							:     str       	=      [192, 768, 1_024]

       lowercase							:     Union[str, Any]       	=      CvtForImageClassification(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				)
       lowercase							:     Optional[Any]       	=      image_size
       lowercase							:     Union[str, Any]       	=      torch.load(SCREAMING_SNAKE_CASE__					,	map_location=torch.device("""cpu"""				)				)

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

       for idx in range(len(config.depth				)				):
              if config.cls_token[idx]:
                     lowercase							:     Optional[Any]       	=      list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE__				)
              lowercase							:     str       	=      list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE__				)
              for cnt in range(config.depth[idx]				):
                     lowercase							:     List[str]       	=      list_of_state_dict + attention(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  lowercase  :					Tuple						    =							argparse.ArgumentParser()
  parser.add_argument(
      """--cvt_model""",
      default="""cvt-w24""",
      type=str,
      help="""Name of the cvt model you'd like to convert.""",
  )
  parser.add_argument(
      """--image_size""",
      default=384,
      type=int,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--cvt_file_name""",
      default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
      type=str,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
