

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	__a     (      A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   SwinConfig()
    SCREAMING_SNAKE_CASE										=   swin_name.split("_"       )

    SCREAMING_SNAKE_CASE										=   name_split[1]
    SCREAMING_SNAKE_CASE										=   int(name_split[4]       )
    SCREAMING_SNAKE_CASE										=   int(name_split[3][-1]       )

    if model_size == "tiny":
        SCREAMING_SNAKE_CASE										=   96
        SCREAMING_SNAKE_CASE										=   (2, 2, 6, 2)
        SCREAMING_SNAKE_CASE										=   (3, 6, 12, 24)
    elif model_size == "small":
        SCREAMING_SNAKE_CASE										=   96
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (3, 6, 12, 24)
    elif model_size == "base":
        SCREAMING_SNAKE_CASE										=   128
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (4, 8, 16, 32)
    else:
        SCREAMING_SNAKE_CASE										=   192
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (6, 12, 24, 48)

    if "in22k" in swin_name:
        SCREAMING_SNAKE_CASE										=   21841
    else:
        SCREAMING_SNAKE_CASE										=   1000
        SCREAMING_SNAKE_CASE										=   "huggingface/label-files"
        SCREAMING_SNAKE_CASE										=   "imagenet-1k-id2label.json"
        SCREAMING_SNAKE_CASE										=   json.load(open(hf_hub_download(A__			,  A__			,  repo_type="dataset"       )			,  "r"       )       )
        SCREAMING_SNAKE_CASE										=   {int(A__       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE										=   idalabel
        SCREAMING_SNAKE_CASE										=   {v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE										=   img_size
    SCREAMING_SNAKE_CASE										=   num_classes
    SCREAMING_SNAKE_CASE										=   embed_dim
    SCREAMING_SNAKE_CASE										=   depths
    SCREAMING_SNAKE_CASE										=   num_heads
    SCREAMING_SNAKE_CASE										=   window_size

    return config





def 	__a     (      A__			:  List[str]       ):
    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("patch_embed.proj"			,  "embeddings.patch_embeddings.projection"       )
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("patch_embed.norm"			,  "embeddings.norm"       )
    if "layers" in name:
        SCREAMING_SNAKE_CASE										=   "encoder." + name
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("attn.proj"			,  "attention.output.dense"       )
    if "attn" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("attn"			,  "attention.self"       )
    if "norm1" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("norm1"			,  "layernorm_before"       )
    if "norm2" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("norm2"			,  "layernorm_after"       )
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("mlp.fc1"			,  "intermediate.dense"       )
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("mlp.fc2"			,  "output.dense"       )

    if name == "norm.weight":
        SCREAMING_SNAKE_CASE										=   "layernorm.weight"
    if name == "norm.bias":
        SCREAMING_SNAKE_CASE										=   "layernorm.bias"

    if "head" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("head"			,  "classifier"       )
    else:
        SCREAMING_SNAKE_CASE										=   "swin." + name

    return name





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Any       ):
    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE										=   orig_state_dict.pop(A__       )

        if "mask" in key:
            continue
        elif "qkv" in key:
            SCREAMING_SNAKE_CASE										=   key.split("."       )
            SCREAMING_SNAKE_CASE										=   int(key_split[1]       )
            SCREAMING_SNAKE_CASE										=   int(key_split[3]       )
            SCREAMING_SNAKE_CASE										=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

    return orig_state_dict





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   timm.create_model(A__			,  pretrained=A__       )
    timm_model.eval()

    SCREAMING_SNAKE_CASE										=   get_swin_config(A__       )
    SCREAMING_SNAKE_CASE										=   SwinForImageClassification(A__       )
    model.eval()

    SCREAMING_SNAKE_CASE										=   convert_state_dict(timm_model.state_dict()			,  A__       )
    model.load_state_dict(A__       )

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

    SCREAMING_SNAKE_CASE										=   AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"			,  "-"       )       )       )
    SCREAMING_SNAKE_CASE										=   Image.open(requests.get(A__			,  stream=A__       ).raw       )
    SCREAMING_SNAKE_CASE										=   image_processor(images=A__			,  return_tensors="pt"       )

    SCREAMING_SNAKE_CASE										=   timm_model(inputs["pixel_values"]       )
    SCREAMING_SNAKE_CASE										=   model(**A__       ).logits

    assert torch.allclose(A__			,  A__			,  atol=1E-3       )

    print(F"Saving model {swin_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 __name__ == "__main__":
    __A			:					Any						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--swin_name',
        default='swin_tiny_patch4_window7_224',
        type=str,
        help='Name of the Swin timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    __A			:					List[Any]						=			parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp



@slow
@require_flax
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )



