




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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					(	_a  ,unittest.TestCase					):
							lowercase_           =       KandinskyVaaInpaintPipeline
							lowercase_           =       ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							lowercase_           =       [
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							lowercase_           =       [
							    'generator',
							    'height',
							    'width',
							    'latents',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							lowercase_           =       False







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							List[Any]:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						str      )    ->							Optional[Any]:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														return self.time_input_dim







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


														"""simple docstring"""


														return self.time_input_dim * 4







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


														"""simple docstring"""


														return 1_00







							@property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       {
														    '''in_channels''': 9,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 8,
														    '''addition_embed_type''': '''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''': '''image_proj''',
														    '''cross_attention_dim''': self.cross_attention_dim,
														    '''attention_head_dim''': 4,
														    '''resnet_time_scale_shift''': '''scale_shift''',
														    '''class_embed_type''': None,
														}

														_a			  =       UNetaDConditionModel(**lowerCAmelCase_      )
														return model







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


														"""simple docstring"""


														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 :						Optional[int]      )    ->							str:


														"""simple docstring"""


														torch.manual_seed(0      )
														_a			  =       VQModel(**self.dummy_movq_kwargs      )
														return model







							def      __lowerCAmelCase    (						self :						int      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.dummy_unet
														_a			  =       self.dummy_movq

														_a			  =       DDIMScheduler(
														    num_train_timesteps=10_00							,    beta_schedule='''linear'''							,    beta_start=0.0_0_0_8_5							,    beta_end=0.0_1_2							,    clip_sample=lowerCAmelCase_							,    set_alpha_to_one=lowerCAmelCase_							,    steps_offset=1							,    prediction_type='''epsilon'''							,    thresholding=lowerCAmelCase_							,    )

														_a			  =       {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						Tuple=0      )    ->							Any:


														"""simple docstring"""


														_a			  =       floats_tensor((1, self.text_embedder_hidden_size)							,    rng=random.Random(lowerCAmelCase_      )      ).to(lowerCAmelCase_      )
														_a			  =       floats_tensor((1, self.text_embedder_hidden_size)							,    rng=random.Random(seed + 1      )      ).to(
														    lowerCAmelCase_      )
														# create init_image
														_a			  =       floats_tensor((1, 3, 64, 64)							,    rng=random.Random(lowerCAmelCase_      )      ).to(lowerCAmelCase_      )
														_a			  =       image.cpu().permute(0							,    2							,    3							,    1      )[0]
														_a			  =       Image.fromarray(np.uinta(lowerCAmelCase_      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )
														# create mask
														_a			  =       np.ones((64, 64)							,    dtype=np.floataa      )
														_a			  =       0

														if str(lowerCAmelCase_      ).startswith('''mps'''      ):
																					_a			  =       torch.manual_seed(lowerCAmelCase_      )
														else:
																					_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )
														_a			  =       {
														    '''image''': init_image,
														    '''mask_image''': mask,
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 4.0,
														    '''output_type''': '''np''',
														}
														return inputs







							def      __lowerCAmelCase    (						self :						int      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       pipe(**self.get_dummy_inputs(lowerCAmelCase_      )      )
														_a			  =       output.images

														_a			  =       pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_      )							,    return_dict=lowerCAmelCase_							,    )[0]

														_a			  =       image[0, -3:, -3:, -1]
														_a			  =       image_from_tuple[0, -3:, -3:, -1]

														print(F'image.shape {image.shape}'      )

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

														_a			  =       np.array(
														    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]      )

														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()}'







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							str:


														"""simple docstring"""


														super().test_inference_batch_single_identical(expected_max_diff=3e-3      )



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







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Optional[Any]:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

														_a			  =       load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
														_a			  =       np.ones((7_68, 7_68)							,    dtype=np.floataa      )
														_a			  =       0

														_a			  =       '''a hat'''

														_a			  =       KandinskyVaaPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''							,    torch_dtype=torch.floataa      )
														pipe_prior.to(lowerCAmelCase_      )

														_a			  =       KandinskyVaaInpaintPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''							,    torch_dtype=torch.floataa      )
														_a			  =       pipeline.to(lowerCAmelCase_      )
														pipeline.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       torch.Generator(device='''cpu'''      ).manual_seed(0      )
														_a  ,							_a			  =       pipe_prior(
														    lowerCAmelCase_							,    generator=lowerCAmelCase_							,    num_inference_steps=5							,    negative_prompt=''''''							,    ).to_tuple()

														_a			  =       pipeline(
														    image=lowerCAmelCase_							,    mask_image=lowerCAmelCase_							,    image_embeds=lowerCAmelCase_							,    negative_image_embeds=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    num_inference_steps=1_00							,    height=7_68							,    width=7_68							,    output_type='''np'''							,    )

														_a			  =       output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,    lowerCAmelCase_      )







'''simple docstring'''


import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case   :   Any     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


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

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
							        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
							        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
							        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
							        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
							        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
							    ]   )

							return rename_keys
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							for i in range(encoder_config.num_hidden_layers   ):
														# queries, keys and values (only weights, no biases)
														_a			  =       state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight'   )

														_a			  =       in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														_a			  =       in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														_a			  =       in_proj_weight[
														    -encoder_config.hidden_size :, :
														]
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       dct.pop(UpperCamelCase   )
							_a			  =       val
def 	snake_case_					(UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


							if "handwritten" in checkpoint_url:
														_a			  =       '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														_a			  =       '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
							_a			  =       Image.open(requests.get(UpperCamelCase				,		stream=UpperCamelCase   ).raw   ).convert('''RGB'''   )
							return im
@torch.no_grad()
def 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       ViTConfig(image_size=384				,		qkv_bias=UpperCamelCase   )
							_a			  =       TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														_a			  =       768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														_a			  =       1024
														_a			  =       4096
														_a			  =       24
														_a			  =       16
														_a			  =       1024
							else:
														raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''   )

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														_a			  =       False
														_a			  =       '''relu'''
														_a			  =       1024
														_a			  =       True
														_a			  =       False
														_a			  =       False

							# load HuggingFace model
							_a			  =       ViTModel(UpperCamelCase				,		add_pooling_layer=UpperCamelCase   )
							_a			  =       TrOCRForCausalLM(UpperCamelCase   )
							_a			  =       VisionEncoderDecoderModel(encoder=UpperCamelCase				,		decoder=UpperCamelCase   )
							model.eval()

							# load state_dict of original model, rename some keys
							_a			  =       torch.hub.load_state_dict_from_url(UpperCamelCase				,		map_location='''cpu'''				,		check_hash=UpperCamelCase   )['''model''']

							_a			  =       create_rename_keys(UpperCamelCase				,		UpperCamelCase   )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							read_in_q_k_v(UpperCamelCase				,		UpperCamelCase   )

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														_a			  =       state_dict.pop(UpperCamelCase   )
														if key.startswith('''decoder'''   ) and "output_projection" not in key:
																					_a			  =       val
														else:
																					_a			  =       val

    # load state dict
							model.load_state_dict(UpperCamelCase   )

							# Check outputs on an image
							_a			  =       ViTImageProcessor(size=encoder_config.image_size   )
							_a			  =       RobertaTokenizer.from_pretrained('''roberta-large'''   )
							_a			  =       TrOCRProcessor(UpperCamelCase				,		UpperCamelCase   )

							_a			  =       processor(images=prepare_img(UpperCamelCase   )				,		return_tensors='''pt'''   ).pixel_values

							# verify logits
							_a			  =       torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
							_a			  =       model(pixel_values=UpperCamelCase				,		decoder_input_ids=UpperCamelCase   )
							_a			  =       outputs.logits

							_a			  =       torch.Size([1, 1, 5_0265]   )
							if "trocr-base-handwritten" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]   )
							elif "trocr-large-handwritten" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]   )
							elif "trocr-base-printed" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]   )
							elif "trocr-large-printed" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]   )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]				,		UpperCamelCase				,		atol=1e-3   ), "First elements of logits not as expected"

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							print(f'Saving model to {pytorch_dump_folder_path}'   )
							model.save_pretrained(UpperCamelCase   )
							print(f'Saving processor to {pytorch_dump_folder_path}'   )
							processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
				_snake_case   :   Tuple     							=	argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_url',
				    default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
				    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.'
				)
				_snake_case   :   Dict     							=	parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

