
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  			=					logging.get_logger(__name__)




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					str					):


				'''simple docstring'''
				lowercase_       =					[]
				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 				a						(							snake_case__:					int						,   snake_case__:					Dict					):


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

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




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Optional[Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				lowercase_       =					dct.pop(snake_case__					)
				lowercase_       =					val




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				if "handwritten" in checkpoint_url:
								lowercase_       =					'''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_       =					'''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_       =					Image.open(requests.get(snake_case__						,   stream=snake_case__					).raw					).convert('''RGB'''					)
				return im




@torch.no_grad()
def 				a						(							snake_case__:					List[Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				lowercase_       =					ViTConfig(image_size=384						,   qkv_bias=snake_case__					)
				lowercase_       =					TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								lowercase_       =					768
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								lowercase_       =					1_024
								lowercase_       =					4_096
								lowercase_       =					24
								lowercase_       =					16
								lowercase_       =					1_024
				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_       =					False
								lowercase_       =					'''relu'''
								lowercase_       =					1_024
								lowercase_       =					True
								lowercase_       =					False
								lowercase_       =					False

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

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

				lowercase_       =					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_       =					state_dict.pop(snake_case__					)
								if key.startswith('''decoder'''					) and "output_projection" not in key:
												lowercase_       =					val
								else:
												lowercase_       =					val

    # load state dict
				model.load_state_dict(snake_case__					)

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

				lowercase_       =					processor(images=prepare_img(snake_case__					)						,   return_tensors='''pt'''					).pixel_values

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

				lowercase_       =					torch.Size([1, 1, 50_265]					)
				if "trocr-base-handwritten" in checkpoint_url:
								lowercase_       =					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:
								lowercase_       =					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:
								lowercase_       =					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:
								lowercase_       =					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]						,   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__":
						__a  			=					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  			=					parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




