'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__lowerCAmelCase	:       Union[str, Any]						       =	logging.get_logger(__name__)  # pylint: disable=invalid-name






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Optional[int]     ,		__a				:							WhisperForConditionalGeneration     ,		__a				:							WhisperProcessor     ,		__a				:							AutoencoderKL     ,		__a				:							CLIPTextModel     ,		__a				:							CLIPTokenizer     ,		__a				:							UNetaDConditionModel     ,		__a				:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,		__a				:							StableDiffusionSafetyChecker     ,		__a				:							CLIPImageProcessor     ,		)							->	Any:
								super().__init__()

								if safety_checker is None:
									logger.warning(
									    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
									    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
									    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
									    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
									    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
									    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''	)

								self.register_modules(
								    speech_model=__a     ,		speech_processor=__a     ,		vae=__a     ,		text_encoder=__a     ,		tokenizer=__a     ,		unet=__a     ,		scheduler=__a     ,		feature_extractor=__a     ,		)





							def       snake_case__						(      self				:							Any     ,		__a				:							Optional[Union[str, int]] = "auto"	)							->	Dict:
								if slice_size == "auto":
									__UpperCAmelCase							=			self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__a	)





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






							@torch.no_grad()
							def __call__(      self				:							Optional[int]     ,		__a				:							int     ,		__a				:							Union[str, Any]=1_6_0_0_0     ,		__a				:							int = 5_1_2     ,		__a				:							int = 5_1_2     ,		__a				:							int = 5_0     ,		__a				:							float = 7.5     ,		__a				:							Optional[Union[str, List[str]]] = None     ,		__a				:							Optional[int] = 1     ,		__a				:							float = 0.0     ,		__a				:							Optional[torch.Generator] = None     ,		__a				:							Optional[torch.FloatTensor] = None     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		__a				:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__a				:							int = 1     ,		**__a				:							Optional[int]     ,		)							->	Tuple:
								__UpperCAmelCase							=			self.speech_processor.feature_extractor(
								    __a     ,		return_tensors='''pt'''     ,		sampling_rate=__a	).input_features.to(self.device	)
								__UpperCAmelCase							=			self.speech_model.generate(__a     ,		max_length=4_8_0_0_0_0	)

								__UpperCAmelCase							=			self.speech_processor.tokenizer.batch_decode(__a     ,		skip_special_tokens=__a     ,		normalize=__a	)[
								    0
								]

								if isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			1
								elif isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			len(__a	)
								else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__a	)}"""	)

								if height % 8 != 0 or width % 8 != 0:
									raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(__a     ,		__a	) or callback_steps <= 0)
								):
									raise ValueError(
									    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    f""" {type(__a	)}."""	)

								# get prompt text embeddings
								__UpperCAmelCase							=			self.tokenizer(
								    __a     ,		padding='''max_length'''     ,		max_length=self.tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
								__UpperCAmelCase							=			text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									__UpperCAmelCase							=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
									logger.warning(
									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""	)
									__UpperCAmelCase							=			text_input_ids[:, : self.tokenizer.model_max_length]
								__UpperCAmelCase							=			self.text_encoder(text_input_ids.to(self.device	)	)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			text_embeddings.shape
								__UpperCAmelCase							=			text_embeddings.repeat(1     ,		__a     ,		1	)
								__UpperCAmelCase							=			text_embeddings.view(bs_embed * num_images_per_prompt     ,		__a     ,		-1	)

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								__UpperCAmelCase							=			guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									__UpperCAmelCase							=			42
									if negative_prompt is None:
										__UpperCAmelCase							=			[''''''] * batch_size
									elif type(__a	) is not type(__a	):
										raise TypeError(
										    f"""`negative_prompt` should be the same type to `prompt`, but got {type(__a	)} !="""
										    f""" {type(__a	)}."""	)
									elif isinstance(__a     ,		__a	):
										__UpperCAmelCase							=			[negative_prompt]
									elif batch_size != len(__a	):
										raise ValueError(
										    f"""`negative_prompt`: {negative_prompt} has batch size {len(__a	)}, but `prompt`:"""
										    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
										    ''' the batch size of `prompt`.'''	)
									else:
										__UpperCAmelCase							=			negative_prompt

									__UpperCAmelCase							=			text_input_ids.shape[-1]
									__UpperCAmelCase							=			self.tokenizer(
									    __a     ,		padding='''max_length'''     ,		max_length=__a     ,		truncation=__a     ,		return_tensors='''pt'''     ,		)
									__UpperCAmelCase							=			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									__UpperCAmelCase							=			uncond_embeddings.shape[1]
									__UpperCAmelCase							=			uncond_embeddings.repeat(1     ,		__a     ,		1	)
									__UpperCAmelCase							=			uncond_embeddings.view(batch_size * num_images_per_prompt     ,		__a     ,		-1	)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									__UpperCAmelCase							=			torch.cat([uncond_embeddings, text_embeddings]	)

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								__UpperCAmelCase							=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								__UpperCAmelCase							=			text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not exist on mps
										__UpperCAmelCase							=			torch.randn(__a     ,		generator=__a     ,		device='''cpu'''     ,		dtype=__a	).to(
										    self.device	)
									else:
										__UpperCAmelCase							=			torch.randn(__a     ,		generator=__a     ,		device=self.device     ,		dtype=__a	)
								else:
									if latents.shape != latents_shape:
										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
									__UpperCAmelCase							=			latents.to(self.device	)

								# set timesteps
								self.scheduler.set_timesteps(__a	)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								__UpperCAmelCase							=			self.scheduler.timesteps.to(self.device	)

								# scale the initial noise by the standard deviation required by the scheduler
								__UpperCAmelCase							=			latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								__UpperCAmelCase							=			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
								__UpperCAmelCase							=			{}
								if accepts_eta:
									__UpperCAmelCase							=			eta

								for i, t in enumerate(self.progress_bar(__a	)	):
									# expand the latents if we are doing classifier free guidance
									__UpperCAmelCase							=			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
									__UpperCAmelCase							=			self.scheduler.scale_model_input(__a     ,		__a	)

									# predict the noise residual
									__UpperCAmelCase							=			self.unet(__a     ,		__a     ,		encoder_hidden_states=__a	).sample

									# perform guidance
									if do_classifier_free_guidance:
										__UpperCAmelCase				,			__UpperCAmelCase							=			noise_pred.chunk(2	)
										__UpperCAmelCase							=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									__UpperCAmelCase							=			self.scheduler.step(__a     ,		__a     ,		__a     ,		**__a	).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
										callback(__a     ,		__a     ,		__a	)

								__UpperCAmelCase							=			1 / 0.1_8_2_1_5 * latents
								__UpperCAmelCase							=			self.vae.decode(__a	).sample

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

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

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

								if not return_dict:
									return image

								return StableDiffusionPipelineOutput(images=__a     ,		nsfw_content_detected=__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)
