


'''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()
__magic_name__ : Tuple					=logging.get_logger(__name__)




def   __snake_case							(							lowerCamelCase_			:					Dict						,	lowerCamelCase_			:					List[Any]    ):
				'''simple docstring'''




				__magic_name__							=			[]
				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							(							lowerCamelCase_			:					Any						,	lowerCamelCase_			:					Optional[Any]    ):
				'''simple docstring'''




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

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




def   __snake_case							(							lowerCamelCase_			:					Any						,	lowerCamelCase_			:					Tuple						,	lowerCamelCase_			:					Optional[Any]    ):
				'''simple docstring'''




				__magic_name__							=			dct.pop(lowerCamelCase_    )
				__magic_name__							=			val




def   __snake_case							(							lowerCamelCase_			:					Any    ):
				'''simple docstring'''




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




@torch.no_grad()
def   __snake_case							(							lowerCamelCase_			:					Optional[int]						,	lowerCamelCase_			:					List[Any]    ):
				'''simple docstring'''




				__magic_name__							=			ViTConfig(image_size=384						,	qkv_bias=lowerCamelCase_    )
				__magic_name__							=			TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								__magic_name__							=			768
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								__magic_name__							=			1024
								__magic_name__							=			4096
								__magic_name__							=			24
								__magic_name__							=			16
								__magic_name__							=			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:
								__magic_name__							=			False
								__magic_name__							=			"relu"
								__magic_name__							=			1024
								__magic_name__							=			True
								__magic_name__							=			False
								__magic_name__							=			False

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

				# load state_dict of original model, rename some keys
				__magic_name__							=			torch.hub.load_state_dict_from_url(lowerCamelCase_						,	map_location="cpu"						,	check_hash=lowerCamelCase_    )["model"]

				__magic_name__							=			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():
								__magic_name__							=			state_dict.pop(lowerCamelCase_    )
								if key.startswith("decoder"    ) and "output_projection" not in key:
												__magic_name__							=			val
								else:
												__magic_name__							=			val

    # load state dict
				model.load_state_dict(lowerCamelCase_    )

				# Check outputs on an image
				__magic_name__							=			ViTImageProcessor(size=encoder_config.image_size    )
				__magic_name__							=			RobertaTokenizer.from_pretrained("roberta-large"    )
				__magic_name__							=			TrOCRProcessor(lowerCamelCase_						,	lowerCamelCase_    )

				__magic_name__							=			processor(images=prepare_img(lowerCamelCase_    )						,	return_tensors="pt"    ).pixel_values

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

				__magic_name__							=			torch.Size([1, 1, 5_0265]    )
				if "trocr-base-handwritten" in checkpoint_url:
								__magic_name__							=			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:
								__magic_name__							=			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:
								__magic_name__							=			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:
								__magic_name__							=			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]						,	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__":
						__magic_name__ : List[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.'
						)
						__magic_name__ : List[Any]					=parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp







@slow
@require_flax
class 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a							,				a					)       ->   Optional[int]:


								'''simple docstring'''

								return F'gaussian_noise_s={seed}_shape={"_".join([str(a					) for s in shape]					)}.npy'

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								super().tearDown()
								gc.collect()

						def 			A_					(      self							,				a=0							,				a=(4, 4, 64, 64)							,				a=False					)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return image

						def 			A_					(      self							,				a=False							,				a="CompVis/stable-diffusion-v1-4"					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						"""bf16""" if fpaa else None

								_UpperCamelCase     ,						_UpperCamelCase         =						FlaxUNetaDConditionModel.from_pretrained(
								    a							,				subfolder="""unet"""							,				dtype=a							,				revision=a					)
								return model, params

						def 			A_					(      self							,				a=0							,				a=(4, 77, 7_68)							,				a=False					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						jnp.bfloataa if fpaa else jnp.floataa
								_UpperCamelCase         =						jnp.array(load_hf_numpy(self.get_file_format(a							,				a					)					)							,				dtype=a					)
								return hidden_states

						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
						        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
						        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						jnp.array(a							,				dtype=jnp.floataa					)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(a							,				a							,				atol=1e-2					)







						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
						        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
						        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]					)
						def 			A_					(      self							,				a							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase     ,						_UpperCamelCase         =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,				fpaa=a					)
								_UpperCamelCase         =						self.get_latents(a							,				shape=(4, 4, 96, 96)							,				fpaa=a					)
								_UpperCamelCase         =						self.get_encoder_hidden_states(a							,				shape=(4, 77, 10_24)							,				fpaa=a					)

								_UpperCamelCase         =						model.apply(
								    {"""params""": params}							,				a							,				jnp.array(a							,				dtype=jnp.intaa					)							,				encoder_hidden_states=a							,				).sample

								assert sample.shape == latents.shape

								_UpperCamelCase         =						jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())					)							,				dtype=jnp.floataa					)
								_UpperCamelCase         =						jnp.array(a							,				dtype=jnp.floataa					)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(a							,				a							,				atol=1e-2					)



