

"""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()
__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		int    							=      []
	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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	for i in range(encoder_config.num_hidden_layers					):
		# queries, keys and values (only weights, no biases)
		lowerCAmelCase__      :		int    							=      state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight'					)

		lowerCAmelCase__      :		Any    							=      in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		lowerCAmelCase__      :		str    							=      in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		lowerCAmelCase__      :		Any    							=      in_proj_weight[
		    -encoder_config.hidden_size :, :
		]

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      dct.pop(A_					)
	lowerCAmelCase__      :		int    							=      val

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

@torch.no_grad()
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Any    							=      ViTConfig(image_size=3_84		,	qkv_bias=A_					)
	lowerCAmelCase__      :		Any    							=      TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		lowerCAmelCase__      :		Tuple    							=      7_68
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		lowerCAmelCase__      :		Dict    							=      10_24
		lowerCAmelCase__      :		List[str]    							=      40_96
		lowerCAmelCase__      :		Any    							=      24
		lowerCAmelCase__      :		Optional[int]    							=      16
		lowerCAmelCase__      :		int    							=      10_24
	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:
		lowerCAmelCase__      :		int    							=      False
		lowerCAmelCase__      :		Dict    							=      '''relu'''
		lowerCAmelCase__      :		List[str]    							=      10_24
		lowerCAmelCase__      :		Optional[Any]    							=      True
		lowerCAmelCase__      :		List[Any]    							=      False
		lowerCAmelCase__      :		List[Any]    							=      False

	# load HuggingFace model
	lowerCAmelCase__      :		Union[str, Any]    							=      ViTModel(A_		,	add_pooling_layer=A_					)
	lowerCAmelCase__      :		str    							=      TrOCRForCausalLM(A_					)
	lowerCAmelCase__      :		int    							=      VisionEncoderDecoderModel(encoder=A_		,	decoder=A_					)
	model.eval()

	# load state_dict of original model, rename some keys
	lowerCAmelCase__      :		List[Any]    							=      torch.hub.load_state_dict_from_url(A_		,	map_location='''cpu'''		,	check_hash=A_					)['''model''']

	lowerCAmelCase__      :		Any    							=      create_rename_keys(A_		,	A_					)
	for src, dest in rename_keys:
		rename_key(A_		,	A_		,	A_					)
	read_in_q_k_v(A_		,	A_					)

	# 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():
		lowerCAmelCase__      :		Any    							=      state_dict.pop(A_					)
		if key.startswith('''decoder'''					) and "output_projection" not in key:
			lowerCAmelCase__      :		Any    							=      val
		else:
			lowerCAmelCase__      :		List[Any]    							=      val

    # load state dict
	model.load_state_dict(A_					)

	# Check outputs on an image
	lowerCAmelCase__      :		Union[str, Any]    							=      ViTImageProcessor(size=encoder_config.image_size					)
	lowerCAmelCase__      :		Optional[Any]    							=      RobertaTokenizer.from_pretrained('''roberta-large'''					)
	lowerCAmelCase__      :		Optional[Any]    							=      TrOCRProcessor(A_		,	A_					)

	lowerCAmelCase__      :		List[Any]    							=      processor(images=prepare_img(A_					)		,	return_tensors='''pt'''					).pixel_values

	# verify logits
	lowerCAmelCase__      :		Dict    							=      torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
	lowerCAmelCase__      :		str    							=      model(pixel_values=A_		,	decoder_input_ids=A_					)
	lowerCAmelCase__      :		Optional[int]    							=      outputs.logits

	lowerCAmelCase__      :		List[str]    							=      torch.Size([1, 1, 5_02_65]					)
	if "trocr-base-handwritten" in checkpoint_url:
		lowerCAmelCase__      :		Union[str, Any]    							=      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:
		lowerCAmelCase__      :		Optional[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:
		lowerCAmelCase__      :		int    							=      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:
		lowerCAmelCase__      :		Optional[Any]    							=      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]		,	A_		,	atol=1e-3					), "First elements of logits not as expected"

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


if __name__ == "__main__":
							__UpperCamelCase    : int				 =						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    : List[str]				 =						parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"t5"
					lowercase__												=			["past_key_values"]
					lowercase__												=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(			self     :							Tuple  ,lowercase_     :							Tuple=3_2_1_2_8  ,lowercase_     :							Union[str, Any]=5_1_2  ,lowercase_     :							Dict=6_4  ,lowercase_     :							Any=2_0_4_8  ,lowercase_     :							Dict=6  ,lowercase_     :							Dict=None  ,lowercase_     :							Dict=8  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							List[Any]=1_2_8  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[Any]=1E-6  ,lowercase_     :							Any=1.0  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Tuple=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Tuple=0  ,lowercase_     :							Optional[Any]=1  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Union[str, Any]    							=      d_kv
						lowerCAmelCase__      :		List[Any]    							=      d_ff
						lowerCAmelCase__      :		int    							=      num_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__      :		Any    							=      num_heads
						lowerCAmelCase__      :		Optional[int]    							=      relative_attention_num_buckets
						lowerCAmelCase__      :		Union[str, Any]    							=      relative_attention_max_distance
						lowerCAmelCase__      :		List[Any]    							=      dropout_rate
						lowerCAmelCase__      :		str    							=      layer_norm_epsilon
						lowerCAmelCase__      :		int    							=      initializer_factor
						lowerCAmelCase__      :		Dict    							=      feed_forward_proj
						lowerCAmelCase__      :		int    							=      use_cache

						lowerCAmelCase__      :		int    							=      self.feed_forward_proj.split('''-'''						)
						lowerCAmelCase__      :		str    							=      act_info[-1]
						lowerCAmelCase__      :		Tuple    							=      act_info[0] == '''gated'''

						if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''						)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__      :		Tuple    							=      '''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_  ,eos_token_id=lowercase_  ,is_encoder_decoder=lowercase_  ,**lowercase_  ,)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		int    							=      {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__      :		Union[str, Any]    							=      '''past_encoder_sequence + sequence'''
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch'''}
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__      :		Any    							=      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_  ,direction='''inputs'''						)

						return common_inputs







					@property
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						return 1_3




