





'''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()
A__							:  Any    =logging.get_logger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[]
      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__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

            _lowerCAmelCase					=				in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            _lowerCAmelCase					=				in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            _lowerCAmelCase					=				in_proj_weight[
                -encoder_config.hidden_size :, :
            ]
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				dct.pop(lowerCAmelCase    )
      _lowerCAmelCase					=				val
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if "handwritten" in checkpoint_url:
            _lowerCAmelCase					=				"""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					=				"""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					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    ).convert("""RGB"""    )
      return im
@torch.no_grad()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				ViTConfig(image_size=3_84				,      qkv_bias=lowerCAmelCase    )
      _lowerCAmelCase					=				TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            _lowerCAmelCase					=				7_68
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
            _lowerCAmelCase					=				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					=				False
            _lowerCAmelCase					=				"""relu"""
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				False
            _lowerCAmelCase					=				False

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

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

      _lowerCAmelCase					=				create_rename_keys(lowerCAmelCase				,      lowerCAmelCase    )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )
      read_in_q_k_v(lowerCAmelCase				,      lowerCAmelCase    )

      # 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					=				state_dict.pop(lowerCAmelCase    )
            if key.startswith("""decoder"""    ) and "output_projection" not in key:
                  _lowerCAmelCase					=				val
            else:
                  _lowerCAmelCase					=				val

    # load state dict
      model.load_state_dict(lowerCAmelCase    )

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

      _lowerCAmelCase					=				processor(images=prepare_img(lowerCAmelCase    )				,      return_tensors="""pt"""    ).pixel_values

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

      _lowerCAmelCase					=				torch.Size([1, 1, 5_02_65]    )
      if "trocr-base-handwritten" in checkpoint_url:
            _lowerCAmelCase					=				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					=				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					=				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					=				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]				,      lowerCAmelCase				,      atol=1e-3    ), "First elements of logits not as expected"

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


if __name__ == "__main__":
      A__							:  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.'''
      )
      A__							:  Optional[Any]    =parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






