


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      StableDiffusionInpaintPipeline
					UpperCAmelCase__ :   Optional[int]   				=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					UpperCAmelCase__ :   int   				=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__ :   Optional[int]   				=      frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					UpperCAmelCase__ :   Tuple   				=      frozenset([]				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Tuple:
												torch.manual_seed(0				)
												a__					=					UNetaDConditionModel(
												    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=__snake_case      ,)
												a__					=					PNDMScheduler(skip_prk_steps=__snake_case				)
												torch.manual_seed(0				)
												a__					=					AutoencoderKL(
												    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=4      ,sample_size=1_28      ,)
												torch.manual_seed(0				)
												a__					=					CLIPTextConfig(
												    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=10_00      ,hidden_act='gelu'      ,projection_dim=5_12      ,)
												a__					=					CLIPTextModel(__snake_case				)
												a__					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												a__					=					{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Union[str, Any]      ,__snake_case       :int=0				) ->   List[Any]:
												# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
												a__					=					floats_tensor((1, 3, 32, 32)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					image.cpu().permute(0      ,2      ,3      ,1				)[0]
												a__					=					Image.fromarray(np.uinta(__snake_case				)				).convert('RGB'				).resize((64, 64)				)
												a__					=					Image.fromarray(np.uinta(image + 4				)				).convert('RGB'				).resize((64, 64)				)
												if str(__snake_case				).startswith('mps'				):
																			a__					=					torch.manual_seed(__snake_case				)
												else:
																			a__					=					torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
												a__					=					{
												    'prompt': 'A painting of a squirrel eating a burger',
												    'image': init_image,
												    'mask_image': mask_image,
												    'generator': generator,
												    'num_inference_steps': 2,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												}
												return inputs
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												a__					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
												a__					=					self.get_dummy_components()
												a__					=					StableDiffusionInpaintPipeline(**__snake_case				)
												a__					=					sd_pipe.to(__snake_case				)
												sd_pipe.set_progress_bar_config(disable=__snake_case				)

												a__					=					self.get_dummy_inputs(__snake_case				)
												a__					=					sd_pipe(**__snake_case				).images
												a__					=					image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												a__					=					np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]				)

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



					def 			lowerCamelCase__(				self       :List[Any]				) ->   Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Dict				) ->   Optional[Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench.npy'				)

												a__					=					'stabilityai/stable-diffusion-2-inpainting'
												a__					=					StableDiffusionInpaintPipeline.from_pretrained(__snake_case      ,safety_checker=__snake_case				)
												pipe.to(__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)
												pipe.enable_attention_slicing()

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 9E-3
					def 			lowerCamelCase__(				self       :List[Any]				) ->   List[Any]:
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'				)

												a__					=					'stabilityai/stable-diffusion-2-inpainting'
												a__					=					StableDiffusionInpaintPipeline.from_pretrained(
												    __snake_case      ,torch_dtype=torch.floataa      ,safety_checker=__snake_case      ,)
												pipe.to(__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)
												pipe.enable_attention_slicing()

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,output_type='np'      ,)
												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 5E-1



					def 			lowerCamelCase__(				self       :List[str]				) ->   Union[str, Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)

												a__					=					'stabilityai/stable-diffusion-2-inpainting'
												a__					=					PNDMScheduler.from_pretrained(__snake_case      ,subfolder='scheduler'				)
												a__					=					StableDiffusionInpaintPipeline.from_pretrained(
												    __snake_case      ,safety_checker=__snake_case      ,scheduler=__snake_case      ,torch_dtype=torch.floataa      ,)
												pipe.to(__snake_case				)
												pipe.set_progress_bar_config(disable=__snake_case				)
												pipe.enable_attention_slicing(1				)
												pipe.enable_sequential_cpu_offload()

												a__					=					'Face of a yellow cat, high resolution, sitting on a park bench'

												a__					=					torch.manual_seed(0				)
												a__					=					pipe(
												    prompt=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,generator=__snake_case      ,num_inference_steps=2      ,output_type='np'      ,)

												a__					=					torch.cuda.max_memory_allocated()
												# make sure that less than 2.65 GB is allocated
												assert mem_bytes < 2.65 * 10**9








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							:	int					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							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(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														a__					=					7_6_8
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														a__					=					1_0_2_4
														a__					=					4_0_9_6
														a__					=					2_4
														a__					=					1_6
														a__					=					1_0_2_4
							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__					=					1_0_2_4
														a__					=					True
														a__					=					False
														a__					=					False

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

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

							a__					=					create_rename_keys(__lowerCAmelCase ,   __lowerCAmelCase       )
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
							read_in_q_k_v(__lowerCAmelCase ,   __lowerCAmelCase       )

							# 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(__lowerCAmelCase       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					a__					=					val
														else:
																					a__					=					val

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

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

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]       )
							elif "trocr-large-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]       )
							elif "trocr-base-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]       )
							elif "trocr-large-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]       )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :1_0] ,   __lowerCAmelCase ,   atol=1E-3       ), "First elements of logits not as expected"

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


if __name__ == "__main__":
						snake_case							:	Optional[int]					      =				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							:	int					      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




