



"""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()
A_						:    Union[str, Any]				=      logging.get_logger(__name__)
def  __snake_case							(	__A      :						str							,		__A      :						Any     )     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     []
				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							(	__A      :						int							,		__A      :						List[Any]     )     ->       Dict:






				'''simple docstring'''
				for i in range(encoder_config.num_hidden_layers     ):
								# queries, keys and values (only weights, no biases)
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""     )

								SCREAMING_SNAKE_CASE						:							Tuple							=     in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE						:							Dict							=     in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE						:							int							=     in_proj_weight[
								    -encoder_config.hidden_size :, :
								]
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Optional[int]							,		__A      :						str     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     dct.pop(__A     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     val
def  __snake_case							(	__A      :						Any     )     ->       Any:






				'''simple docstring'''
				if "handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							int							=     '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:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
				SCREAMING_SNAKE_CASE						:							List[Any]							=     Image.open(requests.get(__A							,		stream=__A     ).raw     ).convert('RGB'     )
				return im
@torch.no_grad()
def  __snake_case							(	__A      :						Dict							,		__A      :						Union[str, Any]     )     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     ViTConfig(image_size=384							,		qkv_bias=__A     )
				SCREAMING_SNAKE_CASE						:							str							=     TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							int							=     768
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								SCREAMING_SNAKE_CASE						:							List[str]							=     1024
								SCREAMING_SNAKE_CASE						:							str							=     4096
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     24
								SCREAMING_SNAKE_CASE						:							Tuple							=     16
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     False
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     'relu'
								SCREAMING_SNAKE_CASE						:							str							=     1024
								SCREAMING_SNAKE_CASE						:							List[Any]							=     True
								SCREAMING_SNAKE_CASE						:							Tuple							=     False
								SCREAMING_SNAKE_CASE						:							Tuple							=     False

				# load HuggingFace model
				SCREAMING_SNAKE_CASE						:							int							=     ViTModel(__A							,		add_pooling_layer=__A     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     TrOCRForCausalLM(__A     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     VisionEncoderDecoderModel(encoder=__A							,		decoder=__A     )
				model.eval()

				# load state_dict of original model, rename some keys
				SCREAMING_SNAKE_CASE						:							Tuple							=     torch.hub.load_state_dict_from_url(__A							,		map_location='cpu'							,		check_hash=__A     )['model']

				SCREAMING_SNAKE_CASE						:							List[Any]							=     create_rename_keys(__A							,		__A     )
				for src, dest in rename_keys:
								rename_key(__A							,		__A							,		__A     )
				read_in_q_k_v(__A							,		__A     )

				# 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():
								SCREAMING_SNAKE_CASE						:							Dict							=     state_dict.pop(__A     )
								if key.startswith('decoder'     ) and "output_projection" not in key:
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     val
								else:
												SCREAMING_SNAKE_CASE						:							int							=     val

    # load state dict
				model.load_state_dict(__A     )

				# Check outputs on an image
				SCREAMING_SNAKE_CASE						:							str							=     ViTImageProcessor(size=encoder_config.image_size     )
				SCREAMING_SNAKE_CASE						:							int							=     RobertaTokenizer.from_pretrained('roberta-large'     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     TrOCRProcessor(__A							,		__A     )

				SCREAMING_SNAKE_CASE						:							Any							=     processor(images=prepare_img(__A     )							,		return_tensors='pt'     ).pixel_values

				# verify logits
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.tensor([[model.config.decoder.decoder_start_token_id]]     )
				SCREAMING_SNAKE_CASE						:							str							=     model(pixel_values=__A							,		decoder_input_ids=__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     outputs.logits

				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.Size([1, 1, 50265]     )
				if "trocr-base-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							str							=     torch.tensor(
								    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]     )
				elif "trocr-large-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							str							=     torch.tensor(
								    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]     )
				elif "trocr-base-printed" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							str							=     torch.tensor(
								    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]     )
				elif "trocr-large-printed" in checkpoint_url:
								SCREAMING_SNAKE_CASE						:							List[str]							=     torch.tensor(
								    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]     )

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

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


if __name__ == "__main__":
							A_						:    Optional[Any]				=      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.'
							)
							A_						:    Tuple				=      parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	str:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							str							=     output.images

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

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]    )

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





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output.images

					SCREAMING_SNAKE_CASE						:							List[str]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_dpmpp_2m'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							str							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe(
					    [prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=15     ,       output_type='np'     ,       use_karras_sigmas=_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							int							=     output.images

					SCREAMING_SNAKE_CASE						:							List[Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							int							=     np.array(
					    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]    )

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





