

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()
lowercase_			=  logging.get_logger(__name__)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				int:





    '''simple docstring'''






    A__   				=       []
    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(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[str]				)							->				List[str]:





    '''simple docstring'''






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

        A__   				=       in_proj_weight[
            : encoder_config.hidden_size, :
        ]
        A__   				=       in_proj_weight[
            encoder_config.hidden_size : encoder_config.hidden_size * 2, :
        ]
        A__   				=       in_proj_weight[
            -encoder_config.hidden_size :, :
        ]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				str:





    '''simple docstring'''






    A__   				=       dct.pop(lowercase_				)
    A__   				=       val
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]				)							->				Union[str, Any]:





    '''simple docstring'''






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





    '''simple docstring'''






    A__   				=       ViTConfig(image_size=384   ,      qkv_bias=lowercase_				)
    A__   				=       TrOCRConfig()

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

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

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

    A__   				=       create_rename_keys(lowercase_   ,      lowercase_				)
    for src, dest in rename_keys:
        rename_key(lowercase_   ,      lowercase_   ,      lowercase_				)
    read_in_q_k_v(lowercase_   ,      lowercase_				)

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

    # load state dict
    model.load_state_dict(lowercase_				)

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

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

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

    A__   				=       torch.Size([1, 1, 50265]				)
    if "trocr-base-handwritten" in checkpoint_url:
        A__   				=       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:
        A__   				=       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:
        A__   				=       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:
        A__   				=       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]   ,      lowercase_   ,      atol=1E-3				), "First elements of logits not as expected"

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


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




import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'bart'
    lowerCamelCase		     =							['past_key_values']
    lowerCamelCase		     =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(				self			:	Tuple,lowercase_			:	Optional[int]=5_0_2_6_5,lowercase_			:	List[str]=1_0_2_4,lowercase_			:	Any=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	str=1_6,lowercase_			:	int=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	Any=1_6,lowercase_			:	Any=0.0,lowercase_			:	str=0.0,lowercase_			:	Optional[Any]="gelu",lowercase_			:	List[str]=1_0_2_4,lowercase_			:	List[Any]=0.1,lowercase_			:	Union[str, Any]=0.0,lowercase_			:	Optional[int]=0.0,lowercase_			:	List[Any]=0.02,lowercase_			:	int=0.0,lowercase_			:	Optional[Any]=False,lowercase_			:	List[Any]=True,lowercase_			:	Union[str, Any]=3,lowercase_			:	int=1,lowercase_			:	int=0,lowercase_			:	List[str]=2,lowercase_			:	Optional[int]=True,lowercase_			:	Tuple=2,lowercase_			:	List[str]=2,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        A__   				=       vocab_size
        A__   				=       max_position_embeddings
        A__   				=       d_model
        A__   				=       encoder_ffn_dim
        A__   				=       encoder_layers
        A__   				=       encoder_attention_heads
        A__   				=       decoder_ffn_dim
        A__   				=       decoder_layers
        A__   				=       decoder_attention_heads
        A__   				=       dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       activation_function
        A__   				=       init_std
        A__   				=       encoder_layerdrop
        A__   				=       decoder_layerdrop
        A__   				=       classifier_dropout
        A__   				=       use_cache
        A__   				=       encoder_layers
        A__   				=       scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_		):
            A__   				=       self.bos_token_id
            warnings.warn(
                F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'		)




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	Dict		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)

            if self.use_past:
                A__   				=       {0: 'batch'}
                A__   				=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                self.fill_with_past_key_values_(lowercase_,direction='inputs'		)
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]		)

        return common_inputs


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super().outputs
        else:
            A__   				=       super(lowercase_,self		).outputs
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__   				=       seq_length if not self.use_past else 1
        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__   				=       {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__   				=       dict(**lowercase_,**lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            A__   				=       common_inputs['decoder_input_ids'].shape[1]
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__   				=       decoder_seq_length + 3
            A__   				=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            A__   				=       torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__   				=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__				,						A__   				=       self.num_layers
            A__   				=       min(lowercase_,lowercase_		)
            A__   				=       max(lowercase_,lowercase_		) - min_num_layers
            A__   				=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

            for _ in range(lowercase_		):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                    )		)
            # TODO: test this.
            A__   				=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
            for _ in range(lowercase_,lowercase_		):
                common_inputs["past_key_values"].append((torch.zeros(lowercase_		), torch.zeros(lowercase_		))		)
        return common_inputs


    def 						snake_case__					(				self			:	List[str],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__   				=       seqlen + 2
            A__				,						A__   				=       self.num_layers
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__   				=       common_inputs['attention_mask'].dtype
            A__   				=       torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__   				=       [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0		)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A__   				=       tokenizer.num_special_tokens_to_add(lowercase_		)
        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_		)

        # Generate dummy inputs according to compute batch and sequence
        A__   				=       [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__   				=       dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        elif self.task == "causal-lm":
            A__   				=       self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__					(				self			:	int,lowercase_			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str		)->   str:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__   				=       super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)

