'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Union[str, Any]     ,		__a				:							TransformeraDModel     ,		__a				:							AutoencoderKL     ,		__a				:							KarrasDiffusionSchedulers     ,		__a				:							Optional[Dict[int, str]] = None     ,		)							->	List[Any]:
								super().__init__()
								self.register_modules(transformer=__a     ,		vae=__a     ,		scheduler=__a	)

								# create a imagenet -> id dictionary for easier use
								__UpperCAmelCase							=			{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(''','''	):
											__UpperCAmelCase							=			int(__a	)
									__UpperCAmelCase							=			dict(sorted(self.labels.items()	)	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Union[str, List[str]]	)							->	List[int]:

								if not isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			list(__a	)

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""	)

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(      self				:							List[Any]     ,		__a				:							List[int]     ,		__a				:							float = 4.0     ,		__a				:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__a				:							int = 5_0     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		)							->	Union[ImagePipelineOutput, Tuple]:
								__UpperCAmelCase							=			len(__a	)
								__UpperCAmelCase							=			self.transformer.config.sample_size
								__UpperCAmelCase							=			self.transformer.config.in_channels

								__UpperCAmelCase							=			randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)     ,		generator=__a     ,		device=self.device     ,		dtype=self.transformer.dtype     ,		)
								__UpperCAmelCase							=			torch.cat([latents] * 2	) if guidance_scale > 1 else latents

								__UpperCAmelCase							=			torch.tensor(__a     ,		device=self.device	).reshape(-1	)
								__UpperCAmelCase							=			torch.tensor([1_0_0_0] * batch_size     ,		device=self.device	)
								__UpperCAmelCase							=			torch.cat([class_labels, class_null]     ,		0	) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(__a	)

								for t in self.progress_bar(self.scheduler.timesteps	):
									if guidance_scale > 1:
										__UpperCAmelCase							=			latent_model_input[: len(__a	) // 2]
										__UpperCAmelCase							=			torch.cat([half, half]     ,		dim=0	)
									__UpperCAmelCase							=			self.scheduler.scale_model_input(__a     ,		__a	)

									__UpperCAmelCase							=			t
									if not torch.is_tensor(__a	):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										__UpperCAmelCase							=			latent_model_input.device.type == '''mps'''
										if isinstance(__a     ,		__a	):
											__UpperCAmelCase							=			torch.floataa if is_mps else torch.floataa
										else:
											__UpperCAmelCase							=			torch.intaa if is_mps else torch.intaa
										__UpperCAmelCase							=			torch.tensor([timesteps]     ,		dtype=__a     ,		device=latent_model_input.device	)
									elif len(timesteps.shape	) == 0:
										__UpperCAmelCase							=			timesteps[None].to(latent_model_input.device	)
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									__UpperCAmelCase							=			timesteps.expand(latent_model_input.shape[0]	)
									# predict noise model_output
									__UpperCAmelCase							=			self.transformer(
									    __a     ,		timestep=__a     ,		class_labels=__a	).sample

									# perform guidance
									if guidance_scale > 1:
										__UpperCAmelCase				,			__UpperCAmelCase							=			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										__UpperCAmelCase				,			__UpperCAmelCase							=			torch.split(__a     ,		len(__a	) // 2     ,		dim=0	)

										__UpperCAmelCase							=			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										__UpperCAmelCase							=			torch.cat([half_eps, half_eps]     ,		dim=0	)

										__UpperCAmelCase							=			torch.cat([eps, rest]     ,		dim=1	)

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										__UpperCAmelCase				,			__UpperCAmelCase							=			torch.split(__a     ,		__a     ,		dim=1	)
									else:
										__UpperCAmelCase							=			noise_pred

									# compute previous image: x_t -> x_t-1
									__UpperCAmelCase							=			self.scheduler.step(__a     ,		__a     ,		__a	).prev_sample

								if guidance_scale > 1:
									__UpperCAmelCase				,			__UpperCAmelCase							=			latent_model_input.chunk(2     ,		dim=0	)
								else:
									__UpperCAmelCase							=			latent_model_input

								__UpperCAmelCase							=			1 / self.vae.config.scaling_factor * latents
								__UpperCAmelCase							=			self.vae.decode(__a	).sample

								__UpperCAmelCase							=			(samples / 2 + 0.5).clamp(0     ,		1	)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								__UpperCAmelCase							=			samples.cpu().permute(0     ,		2     ,		3     ,		1	).float().numpy()

								if output_type == "pil":
									__UpperCAmelCase							=			self.numpy_to_pil(__a	)

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=__a	)

'''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()
__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


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

		__UpperCAmelCase							=			in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    -encoder_config.hidden_size :, :
		]





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	if "handwritten" in checkpoint_url:
		__UpperCAmelCase							=			'''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:
		__UpperCAmelCase							=			'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	__UpperCAmelCase							=			Image.open(requests.get(UpperCamelCase__    ,					stream=UpperCamelCase__       ).raw       ).convert('''RGB'''       )
	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRConfig()

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

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

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

	__UpperCAmelCase							=			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():
		__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

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

	__UpperCAmelCase							=			processor(images=prepare_img(UpperCamelCase__       )    ,					return_tensors='''pt'''       ).pixel_values

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

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]       )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]       )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]       )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]       )

	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]    ,					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__":
					__lowerCAmelCase	:       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."
					)
					__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
