

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[str]										=      """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






     def 	UpperCAmelCase_				(				self		,						_lowercase=0	)->     str:
         UpperCamelCase_			  =						np.random.RandomState(_lowercase	)
         UpperCamelCase_			  =						{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         UpperCamelCase_			  =						PNDMScheduler.from_config(pipe.scheduler.config		,						skip_prk_steps=_lowercase	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         UpperCamelCase_			  =						LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         UpperCamelCase_			  =						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         UpperCamelCase_			  =						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         UpperCamelCase_			  =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						pipe(**_lowercase	).images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCamelCase_			  =						np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						3 * [inputs["prompt"]]

         # forward
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images[0, -3:, -3:, -1]

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						3 * [inputs.pop("prompt"	)]

         UpperCamelCase_			  =						pipe.tokenizer(
             _lowercase		,						padding="max_length"		,						max_length=pipe.tokenizer.model_max_length		,						truncation=_lowercase		,						return_tensors="np"		,						)
         UpperCamelCase_			  =						text_inputs["input_ids"]

         UpperCamelCase_			  =						pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

         UpperCamelCase_			  =						prompt_embeds

         # forward
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,						provider="CPUExecutionProvider"	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						3 * ["this is a negative prompt"]
         UpperCamelCase_			  =						negative_prompt
         UpperCamelCase_			  =						3 * [inputs["prompt"]]

         # forward
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images[0, -3:, -3:, -1]

         UpperCamelCase_			  =						self.get_dummy_inputs()
         UpperCamelCase_			  =						3 * [inputs.pop("prompt"	)]

         UpperCamelCase_			  =						[]
         for p in [prompt, negative_prompt]:
             UpperCamelCase_			  =						pipe.tokenizer(
                 _lowercase		,						padding="max_length"		,						max_length=pipe.tokenizer.model_max_length		,						truncation=_lowercase		,						return_tensors="np"		,						)
             UpperCamelCase_			  =						text_inputs["input_ids"]

             embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]	)

         UpperCamelCase_  ,       UpperCamelCase_			  =						embeds

         # forward
         UpperCamelCase_			  =						pipe(**_lowercase	)
         UpperCamelCase_			  =						output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4






@nightly
@require_onnxruntime
@require_torch_gpu
class 						__magic_name__					(							unittest.TestCase				):






     @property
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






     @property
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						ort.SessionOptions()
         UpperCamelCase_			  =						False
         return options






     def 	UpperCAmelCase_				(				self	)->     Any:
         # using the PNDM scheduler by default
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="onnx"		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         sd_pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						"A painting of a squirrel eating a burger"
         np.random.seed(0	)
         UpperCamelCase_			  =						sd_pipe([prompt]		,						guidance_scale=6.0		,						num_inference_steps=10		,						output_type="np"	)
         UpperCamelCase_			  =						output.images

         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCamelCase_			  =						np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						DDIMScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						subfolder="scheduler"		,						revision="onnx"	)
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						revision="onnx"		,						scheduler=_lowercase		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         sd_pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						"open neural network exchange"
         UpperCamelCase_			  =						np.random.RandomState(0	)
         UpperCamelCase_			  =						sd_pipe([prompt]		,						guidance_scale=7.5		,						num_inference_steps=10		,						generator=_lowercase		,						output_type="np"	)
         UpperCamelCase_			  =						output.images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCamelCase_			  =						np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						subfolder="scheduler"		,						revision="onnx"	)
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						revision="onnx"		,						scheduler=_lowercase		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         sd_pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						"open neural network exchange"
         UpperCamelCase_			  =						np.random.RandomState(0	)
         UpperCamelCase_			  =						sd_pipe([prompt]		,						guidance_scale=7.5		,						num_inference_steps=10		,						generator=_lowercase		,						output_type="np"	)
         UpperCamelCase_			  =						output.images
         UpperCamelCase_			  =						image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCamelCase_			  =						np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						0

         def test_callback_fn(_lowercase		,						_lowercase		,						_lowercase	) -> None:
             UpperCamelCase_			  =						True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 0:
                 assert latents.shape == (1, 4, 64, 64)
                 UpperCamelCase_			  =						latents[0, -3:, -3:, -1]
                 UpperCamelCase_			  =						np.array(
                     [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]	)

                 assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3
             elif step == 5:
                 assert latents.shape == (1, 4, 64, 64)
                 UpperCamelCase_			  =						latents[0, -3:, -3:, -1]
                 UpperCamelCase_			  =						np.array(
                     [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]	)

                 assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3

         UpperCamelCase_			  =						False

         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						revision="onnx"		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						"Andromeda galaxy in a bottle"

         UpperCamelCase_			  =						np.random.RandomState(0	)
         pipe(
             prompt=_lowercase		,						num_inference_steps=5		,						guidance_scale=7.5		,						generator=_lowercase		,						callback=_lowercase		,						callback_steps=1		,						)
         assert test_callback_fn.has_been_called
         assert number_of_steps == 6






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"		,						revision="onnx"		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         assert isinstance(_lowercase		,						_lowercase	)
         assert pipe.safety_checker is None

         UpperCamelCase_			  =						pipe("example prompt"		,						num_inference_steps=2	).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowercase	)
             UpperCamelCase_			  =						OnnxStableDiffusionPipeline.from_pretrained(_lowercase	)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCamelCase_			  =						pipe("example prompt"		,						num_inference_steps=2	).images[0]
         assert image is not None



import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						SwinvaConfig()
    UpperCamelCase_			  =						swinva_name.split("_"			)

    UpperCamelCase_			  =						name_split[1]
    if "to" in name_split[3]:
        UpperCamelCase_			  =						int(name_split[3][-3:]			)
    else:
        UpperCamelCase_			  =						int(name_split[3]			)
    if "to" in name_split[2]:
        UpperCamelCase_			  =						int(name_split[2][-2:]			)
    else:
        UpperCamelCase_			  =						int(name_split[2][6:]			)

    if model_size == "tiny":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 6, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCamelCase_			  =						1_2_8
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(4, 8, 1_6, 3_2)
    else:
        UpperCamelCase_			  =						1_9_2
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(6, 1_2, 2_4, 4_8)

    if "to" in swinva_name:
        UpperCamelCase_			  =						(1_2, 1_2, 1_2, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase_			  =						2_1_8_4_1
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    else:
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						img_size
    UpperCamelCase_			  =						num_classes
    UpperCamelCase_			  =						embed_dim
    UpperCamelCase_			  =						depths
    UpperCamelCase_			  =						num_heads
    UpperCamelCase_			  =						window_size

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "patch_embed.proj" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.proj"			,						"embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.norm"			,						"embeddings.norm"			)
    if "layers" in name:
        UpperCamelCase_			  =						"encoder." + name
    if "attn.proj" in name:
        UpperCamelCase_			  =						name.replace("attn.proj"			,						"attention.output.dense"			)
    if "attn" in name:
        UpperCamelCase_			  =						name.replace("attn"			,						"attention.self"			)
    if "norm1" in name:
        UpperCamelCase_			  =						name.replace("norm1"			,						"layernorm_before"			)
    if "norm2" in name:
        UpperCamelCase_			  =						name.replace("norm2"			,						"layernorm_after"			)
    if "mlp.fc1" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc1"			,						"intermediate.dense"			)
    if "mlp.fc2" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc2"			,						"output.dense"			)
    if "q_bias" in name:
        UpperCamelCase_			  =						name.replace("q_bias"			,						"query.bias"			)
    if "k_bias" in name:
        UpperCamelCase_			  =						name.replace("k_bias"			,						"key.bias"			)
    if "v_bias" in name:
        UpperCamelCase_			  =						name.replace("v_bias"			,						"value.bias"			)
    if "cpb_mlp" in name:
        UpperCamelCase_			  =						name.replace("cpb_mlp"			,						"continuous_position_bias_mlp"			)
    if name == "norm.weight":
        UpperCamelCase_			  =						"layernorm.weight"
    if name == "norm.bias":
        UpperCamelCase_			  =						"layernorm.bias"

    if "head" in name:
        UpperCamelCase_			  =						name.replace("head"			,						"classifier"			)
    else:
        UpperCamelCase_			  =						"swinv2." + name

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_			  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

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

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

    return orig_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						timm.create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    UpperCamelCase_			  =						get_swinva_config(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						SwinvaForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()

    UpperCamelCase_			  =						convert_state_dict(timm_model.state_dict()			,						SCREAMING_SNAKE_CASE_			)
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

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

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

    UpperCamelCase_			  =						timm_model(inputs["pixel_values"]			)
    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			).logits

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

    print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    print(f"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						organization="nandwalritik"			,						commit_message="Add model"			,						)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swinv2_name""",
     default="""swinv2_tiny_patch4_window8_256""",
     type=str,
     help="""Name of the Swinv2 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."""
 )

 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
