
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class     a_       (			SCREAMING_SNAKE_CASE_				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Optional[Any]						=  KandinskyPipeline
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  [
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       __SCREAMING_SNAKE_CASE       :					Tuple						=  ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  [
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       __SCREAMING_SNAKE_CASE       :					Tuple						=  False






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           return 32






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           return 32






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           return self.time_input_dim






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           return self.time_input_dim * 4






       @property
       def 	__lowerCAmelCase     (		self   )					->int:
           return 100






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
           return tokenizer






       @property
       def 	__lowerCAmelCase     (		self   )					->Any:
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = MCLIPConfig(
               numDims=self.cross_attention_dim				,       transformerDimensions=self.text_embedder_hidden_size				,       hidden_size=self.text_embedder_hidden_size				,       intermediate_size=37				,       num_attention_heads=4				,       num_hidden_layers=5				,       vocab_size=1005				,       )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = MultilingualCLIP(SCREAMING_SNAKE_CASE_   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = text_encoder.eval()

           return text_encoder






       @property
       def 	__lowerCAmelCase     (		self   )					->int:
           torch.manual_seed(0   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''text_image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''text_image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           SCREAMING_SNAKE_CASE				:				List[Any]				      = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_   )
           return model






       @property
       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				int				      = VQModel(**self.dummy_movq_kwargs   )
           return model






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.dummy_text_encoder
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.dummy_tokenizer
           SCREAMING_SNAKE_CASE				:				Dict				      = self.dummy_unet
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.dummy_movq

           SCREAMING_SNAKE_CASE				:				List[Any]				      = DDIMScheduler(
               num_train_timesteps=1000				,       beta_schedule='''linear'''				,       beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       clip_sample=SCREAMING_SNAKE_CASE_				,       set_alpha_to_one=SCREAMING_SNAKE_CASE_				,       steps_offset=1				,       prediction_type='''epsilon'''				,       thresholding=SCREAMING_SNAKE_CASE_				,       )

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = {
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }
           return components






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=0   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				int				      = floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = floats_tensor((1, self.cross_attention_dim)				,       rng=random.Random(seed + 1   )   ).to(SCREAMING_SNAKE_CASE_   )
           if str(SCREAMING_SNAKE_CASE_   ).startswith('''mps'''   ):
               SCREAMING_SNAKE_CASE				:				int				      = torch.manual_seed(SCREAMING_SNAKE_CASE_   )
           else:
               SCREAMING_SNAKE_CASE				:				Any				      = torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
           SCREAMING_SNAKE_CASE				:				int				      = {
               '''prompt''': '''horse''',
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''guidance_scale''': 4.0,
               '''num_inference_steps''': 2,
               '''output_type''': '''np''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = '''cpu'''

           SCREAMING_SNAKE_CASE				:				int				      = self.get_dummy_components()

           SCREAMING_SNAKE_CASE				:				List[str]				      = self.pipeline_class(**SCREAMING_SNAKE_CASE_   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = pipe.to(SCREAMING_SNAKE_CASE_   )

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = output.images

           SCREAMING_SNAKE_CASE				:				List[Any]				      = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )				,       return_dict=SCREAMING_SNAKE_CASE_				,       )[0]

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				Any				      = np.array(
               [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]   )

           assert (
               np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
           ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
           ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






@slow
@require_torch_gpu
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				str				      = load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''   )

           SCREAMING_SNAKE_CASE				:				str				      = KandinskyPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-1-prior'''				,       torch_dtype=torch.floataa   )
           pipe_prior.to(SCREAMING_SNAKE_CASE_   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''				,       torch_dtype=torch.floataa   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = pipeline.to(SCREAMING_SNAKE_CASE_   )
           pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

           SCREAMING_SNAKE_CASE				:				Dict				      = '''red cat, 4k photo'''

           SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Generator(device='''cuda'''   ).manual_seed(0   )
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = pipe_prior(
               SCREAMING_SNAKE_CASE_				,       generator=SCREAMING_SNAKE_CASE_				,       num_inference_steps=5				,       negative_prompt=''''''				,       ).to_tuple()

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.Generator(device='''cuda'''   ).manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Dict				      = pipeline(
               SCREAMING_SNAKE_CASE_				,       image_embeds=SCREAMING_SNAKE_CASE_				,       negative_image_embeds=SCREAMING_SNAKE_CASE_				,       generator=SCREAMING_SNAKE_CASE_				,       num_inference_steps=100				,       output_type='''np'''				,       )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = output.images[0]

           assert image.shape == (512, 512, 3)

           assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_   )


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__				:				List[Any]						=      logging.get_logger(__name__)




def 						A_			(				snake_case				: int			,   snake_case				: Tuple=False			,   snake_case				: List[Any]=False			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''

						__UpperCamelCase  	=      []
						for i in range(config.num_hidden_layers			):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")			)
												rename_keys.append(
												    (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")			)
												rename_keys.append(
												    (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")			)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
						        (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
						        (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
						    ]			)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('''mask_token''', '''beit.embeddings.mask_token'''),
												        (
												            '''rel_pos_bias.relative_position_bias_table''',
												            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
												        ),
												        (
												            '''rel_pos_bias.relative_position_index''',
												            '''beit.encoder.relative_position_bias.relative_position_index''',
												        ),
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												    ]			)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
												        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
												        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
												        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
												    ]			)
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
												        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
												        ('''head.weight''', '''classifier.weight'''),
												        ('''head.bias''', '''classifier.bias'''),
												    ]			)

						return rename_keys




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: List[Any]			,   snake_case				: Union[str, Any]=False			,   snake_case				: List[str]=False			)   ->     Union[str, Any]:



						'''simple docstring'''

						for i in range(config.num_hidden_layers			):
												__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''
												# queries, keys and values
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"			)

												__UpperCamelCase  	=      in_proj_weight[
												    : config.hidden_size, :
												]
												__UpperCamelCase  	=      q_bias
												__UpperCamelCase  	=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__UpperCamelCase  	=      in_proj_weight[
												    -config.hidden_size :, :
												]
												__UpperCamelCase  	=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_1"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_2"			)

												__UpperCamelCase  	=      gamma_a
												__UpperCamelCase  	=      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"			)
																		__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"			)

																		__UpperCamelCase  	=      table
																		__UpperCamelCase  	=      index




def 						A_			(				snake_case				: Dict			,   snake_case				: Optional[int]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      dct.pop(snake_case			)
						__UpperCamelCase  	=      val




def 						A_			(				)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			)
						return im




@torch.no_grad()
def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: Any			)   ->     Tuple:



						'''simple docstring'''

						__UpperCamelCase  	=      BeitConfig()
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      '''huggingface/label-files'''
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      21841
												__UpperCamelCase  	=      '''imagenet-22k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9205]
												del idalabel[15027]
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      1000
												__UpperCamelCase  	=      '''imagenet-1k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		__UpperCamelCase  	=      384
												if "512" in checkpoint_url:
																		__UpperCamelCase  	=      512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      150
												__UpperCamelCase  	=      '''ade20k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												__UpperCamelCase  	=      640
												__UpperCamelCase  	=      True
						else:
												raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''			)

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												__UpperCamelCase  	=      1024
												__UpperCamelCase  	=      4096
												__UpperCamelCase  	=      24
												__UpperCamelCase  	=      16
												if "ade20k" in checkpoint_url:
																		__UpperCamelCase  	=      640
																		__UpperCamelCase  	=      [7, 11, 15, 23]
						else:
												raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''			)

						# load state_dict of original model, remove and rename some keys
						__UpperCamelCase  	=      torch.hub.load_state_dict_from_url(snake_case			,   map_location='''cpu'''			,   check_hash=snake_case			)
						__UpperCamelCase  	=      state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

						__UpperCamelCase  	=      create_rename_keys(snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						for src, dest in rename_keys:
												rename_key(snake_case			,   snake_case			,   snake_case			)
						read_in_q_k_v(snake_case			,   snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		__UpperCamelCase  	=      state_dict.pop(snake_case			)
																		if key.startswith('''backbone.fpn'''			):
																								__UpperCamelCase  	=      key.replace('''backbone.fpn'''			,   '''fpn'''			)
																		__UpperCamelCase  	=      val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												__UpperCamelCase  	=      BeitForMaskedImageModeling(snake_case			)
						elif "ade20k" in checkpoint_url:
												__UpperCamelCase  	=      BeitForSemanticSegmentation(snake_case			)
						else:
												__UpperCamelCase  	=      BeitForImageClassification(snake_case			)
						model.eval()
						model.load_state_dict(snake_case			)

						# Check outputs on an image
						if is_semantic:
												__UpperCamelCase  	=      BeitImageProcessor(size=config.image_size			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''			)
												__UpperCamelCase  	=      Image.open(ds[0]['''file''']			)
						else:
												__UpperCamelCase  	=      BeitImageProcessor(
												    size=config.image_size			,   resample=PILImageResampling.BILINEAR			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      prepare_img()

						__UpperCamelCase  	=      image_processor(images=snake_case			,   return_tensors='''pt'''			)
						__UpperCamelCase  	=      encoding['''pixel_values''']

						__UpperCamelCase  	=      model(snake_case			)
						__UpperCamelCase  	=      outputs.logits

						# verify logits
						__UpperCamelCase  	=      torch.Size([1, 1000]			)
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([2.2288, 2.4671, 0.7395]			)
												__UpperCamelCase  	=      2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([1.6881, -0.2787, 0.5901]			)
												__UpperCamelCase  	=      2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.1241, 0.0798, -0.6569]			)
												__UpperCamelCase  	=      285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.2385, -1.0987, -1.0108]			)
												__UpperCamelCase  	=      281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.5303, -0.9484, -0.3147]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.4610, -0.0928, 0.2086]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.4804, 0.6257, -0.1837]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.3062, 0.7261, 0.4852]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]			)
						else:
												raise ValueError('''Can\'t verify logits as model is not supported'''			)

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]			,   snake_case			,   atol=1e-3			), "First elements of logits not as expected"
												else:
																		print('''Predicted class idx:'''			,   logits.argmax(-1			).item()			)
																		assert torch.allclose(
																		    logits[0, :3]			,   snake_case			,   atol=1e-3			), "First elements of logits not as expected"
																		assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

						Path(snake_case			).mkdir(exist_ok=snake_case			)
						print(f"Saving model to {pytorch_dump_folder_path}"			)
						model.save_pretrained(snake_case			)
						print(f"Saving image processor to {pytorch_dump_folder_path}"			)
						image_processor.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Any						=      argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_url",
				    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
				    type=str,
				    help="URL to the original PyTorch checkpoint (.pth file).",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
				)
				lowercase__				:				int						=      parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




