
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()
UpperCamelCase_						:     str	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			str:






						'''simple docstring'''

						_lowercase       :	List[Any]           =   []
						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 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Dict  )   ->			Optional[Any]:






						'''simple docstring'''

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

												_lowercase       :	Dict           =   in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												_lowercase       :	Dict           =   in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												_lowercase       :	Optional[int]           =   in_proj_weight[
												    -encoder_config.hidden_size :, :
												]



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   int  )   ->			List[Any]:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   dct.pop(_snake_case  )
						_lowercase       :	Union[str, Any]           =   val



def 		UpperCamelCase     (       _UpperCAmelCase  :   Tuple  )   ->			Any:






						'''simple docstring'''

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



@torch.no_grad()
def 		UpperCamelCase     (       _UpperCAmelCase  :   int ,			_UpperCAmelCase  :   str  )   ->			str:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   ViTConfig(image_size=384 ,			qkv_bias=_snake_case  )
						_lowercase       :	List[Any]           =   TrOCRConfig()

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

						# load HuggingFace model
						_lowercase       :	int           =   ViTModel(_snake_case ,			add_pooling_layer=_snake_case  )
						_lowercase       :	Any           =   TrOCRForCausalLM(_snake_case  )
						_lowercase       :	List[Any]           =   VisionEncoderDecoderModel(encoder=_snake_case ,			decoder=_snake_case  )
						model.eval()

						# load state_dict of original model, rename some keys
						_lowercase       :	List[str]           =   torch.hub.load_state_dict_from_url(_snake_case ,			map_location="cpu" ,			check_hash=_snake_case  )["model"]

						_lowercase       :	Optional[Any]           =   create_rename_keys(_snake_case ,			_snake_case  )
						for src, dest in rename_keys:
												rename_key(_snake_case ,			_snake_case ,			_snake_case  )
						read_in_q_k_v(_snake_case ,			_snake_case  )

						# 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():
												_lowercase       :	Optional[Any]           =   state_dict.pop(_snake_case  )
												if key.startswith("decoder"  ) and "output_projection" not in key:
																		_lowercase       :	Union[str, Any]           =   val
												else:
																		_lowercase       :	Union[str, Any]           =   val

    # load state dict
						model.load_state_dict(_snake_case  )

						# Check outputs on an image
						_lowercase       :	int           =   ViTImageProcessor(size=encoder_config.image_size  )
						_lowercase       :	Optional[int]           =   RobertaTokenizer.from_pretrained("roberta-large"  )
						_lowercase       :	Optional[int]           =   TrOCRProcessor(_snake_case ,			_snake_case  )

						_lowercase       :	int           =   processor(images=prepare_img(_snake_case  ) ,			return_tensors="pt"  ).pixel_values

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

						_lowercase       :	Union[str, Any]           =   torch.Size([1, 1, 5_0265]  )
						if "trocr-base-handwritten" in checkpoint_url:
												_lowercase       :	str           =   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:
												_lowercase       :	Union[str, Any]           =   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:
												_lowercase       :	Dict           =   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:
												_lowercase       :	int           =   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, :10] ,			_snake_case ,			atol=1e-3  ), "First elements of logits not as expected"

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


if __name__ == "__main__":
							UpperCamelCase_						:     str	       =							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."""
							)
							UpperCamelCase_						:     int	       =							parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""

from manim import *





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  Rectangle(height=0.5       ,				width=0.5						)
       _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0						)

       _A     =  Rectangle(height=0.25       ,				width=0.25						)

       _A     =  [mem.copy() for i in range(6						)]
       _A     =  [mem.copy() for i in range(6						)]
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  Text('CPU'       ,				font_size=24						)
       _A     =  Group(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0.5       ,				aligned_edge=_UpperCAmelCase						)
       cpu.move_to([-2.5, -0.5, 0]						)
       self.add(_UpperCAmelCase						)

       _A     =  [mem.copy() for i in range(4						)]
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  Text('GPU'       ,				font_size=24						)
       _A     =  Group(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0.5       ,				aligned_edge=_UpperCAmelCase						)
       gpu.move_to([-1, -1, 0]						)
       self.add(_UpperCAmelCase						)

       _A     =  [mem.copy() for i in range(6						)]
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)

       _A     =  Text('Model'       ,				font_size=24						)
       _A     =  Group(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0.5       ,				aligned_edge=_UpperCAmelCase						)
       model.move_to([3, -1.0, 0]						)
       self.add(_UpperCAmelCase						)

       _A     =  []
       _A     =  []

       for i, rect in enumerate(_UpperCAmelCase						):
             _A     =  fill.copy().set_fill(_UpperCAmelCase       ,				opacity=0.8						)
             target.move_to(_UpperCAmelCase						)
             model_arr.append(_UpperCAmelCase						)

             _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0.0						).set_fill(_UpperCAmelCase       ,				opacity=0.8						)
             cpu_target.move_to(cpu_left_col_base[i]						)
             model_cpu_arr.append(_UpperCAmelCase						)

       self.add(*_UpperCAmelCase       ,				*_UpperCAmelCase						)

       _A     =  [meta_mem.copy() for i in range(6						)]
       _A     =  [meta_mem.copy() for i in range(6						)]
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  Text('Disk'       ,				font_size=24						)
       _A     =  Group(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0.5       ,				aligned_edge=_UpperCAmelCase						)
       disk.move_to([-4, -1.25, 0]						)
       self.add(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  Square(side_length=2.2						)
       key.move_to([-5, 2, 0]						)

       _A     =  MarkupText(
           F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''       ,				font_size=18       ,				)

       key_text.move_to([-5, 2.4, 0]						)

       self.add(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  MarkupText(
           F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''       ,				font_size=18       ,				)

       blue_text.next_to(_UpperCAmelCase       ,				DOWN * 2.4       ,				aligned_edge=key_text.get_left()						)
       self.add(_UpperCAmelCase						)

       _A     =  MarkupText(
           F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''       ,				font_size=24       ,				)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase						)						)

       _A     =  Square(0.3						)
       input.set_fill(_UpperCAmelCase       ,				opacity=1.0						)
       input.set_stroke(width=0.0						)
       input.next_to(model_base[0]       ,				_UpperCAmelCase       ,				buff=0.5						)

       self.play(Write(_UpperCAmelCase						)						)

       input.generate_target()
       input.target.next_to(model_arr[0]       ,				direction=_UpperCAmelCase       ,				buff=0.02						)
       self.play(MoveToTarget(_UpperCAmelCase						)						)

       self.play(FadeOut(_UpperCAmelCase						)						)

       _A     =  Arrow(start=_UpperCAmelCase       ,				end=_UpperCAmelCase       ,				color=_UpperCAmelCase       ,				buff=0.5						)
       a.next_to(model_arr[0].get_left()       ,				_UpperCAmelCase       ,				buff=0.2						)

       model_cpu_arr[0].generate_target()
       model_cpu_arr[0].target.move_to(gpu_rect[0]						)

       _A     =  MarkupText(
           F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''       ,				font_size=24       ,				)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase       ,				run_time=3						)						)

       _A     =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

       self.play(
           Write(_UpperCAmelCase						)       ,				Circumscribe(model_arr[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(model_cpu_arr[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)
       self.play(MoveToTarget(model_cpu_arr[0]						)						)

       _A     =  a.copy()
       for i in range(6						):
             a_c.next_to(model_arr[i].get_right() + 0.02       ,				_UpperCAmelCase       ,				buff=0.2						)

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02						)

             _A     =  AnimationGroup(
                 FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				MoveToTarget(_UpperCAmelCase       ,				run_time=0.5						)       ,				FadeIn(_UpperCAmelCase       ,				run_time=0.5						)       ,				lag_ratio=0.2						)

             self.play(_UpperCAmelCase						)

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

             if i < 5:
                   model_cpu_arr[i + 1].generate_target()
                   model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
                   if i >= 1:
                         _A     =  0.7

                   self.play(
                       Circumscribe(model_arr[i]       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[i]       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[i + 1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(model_arr[i + 1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)
                   if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						)       ,				MoveToTarget(model_cpu_arr[i + 1]						)       ,				)
                   else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]       ,				run_time=0.7						)       ,				MoveToTarget(model_cpu_arr[i + 1]       ,				run_time=0.7						)       ,				)
             else:
                   model_cpu_arr[i].generate_target()
                   model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
                   input.generate_target()
                   input.target.next_to(model_arr[-1].get_right()       ,				RIGHT + 0.02       ,				buff=0.2						)

                   self.play(
                       Circumscribe(model_arr[-1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[-1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)

                   self.play(MoveToTarget(model_cpu_arr[i]						)						)

             _A     =  a_c
             _A     =  a_c.copy()

       input.generate_target()
       input.target.next_to(model_base[-1]       ,				RIGHT + 0.02       ,				buff=0.5						)
       self.play(
           FadeOut(_UpperCAmelCase						)       ,				FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				)

       _A     =  MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.'''       ,				font_size=24						)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase       ,				run_time=3						)       ,				MoveToTarget(_UpperCAmelCase						)						)

       self.wait()

