
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					:   Tuple							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       []
					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__     :							Union[str, Any]      , UpperCAmelCase__     :							Any  ):
					for i in range(encoder_config.num_hidden_layers  ):
										# queries, keys and values (only weights, no biases)
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"  )

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





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





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Union[str, Any]  ):
					SCREAMING_SNAKE_CASE    			=       ViTConfig(image_size=3_8_4      , qkv_bias=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       TrOCRConfig()

					# size of the architecture
					if "base" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7_6_8
					elif "large" in checkpoint_url:
										# use ViT-large encoder
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       False
										SCREAMING_SNAKE_CASE    			=       "relu"
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       False
										SCREAMING_SNAKE_CASE    			=       False

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

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

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

    # load state dict
					model.load_state_dict(UpperCAmelCase__  )

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

					SCREAMING_SNAKE_CASE    			=       processor(images=prepare_img(UpperCAmelCase__  )      , return_tensors="pt"  ).pixel_values

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

					SCREAMING_SNAKE_CASE    			=       torch.Size([1, 1, 5_0_2_6_5]  )
					if "trocr-base-handwritten" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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, :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					:   List[str]							=		parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy




