



"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCAmelCase							:       List[str]          =       {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       str          =       ['''MobileViTFeatureExtractor''']
					__lowerCAmelCase							:       Optional[int]          =       ['''MobileViTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       Optional[int]          =       [
					    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MobileViTForImageClassification''',
					    '''MobileViTForSemanticSegmentation''',
					    '''MobileViTModel''',
					    '''MobileViTPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase							:       Union[str, Any]          =       [
					    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFMobileViTForImageClassification''',
					    '''TFMobileViTForSemanticSegmentation''',
					    '''TFMobileViTModel''',
					    '''TFMobileViTPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilevit import MobileViTFeatureExtractor
										from .image_processing_mobilevit import MobileViTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevit import (
										    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTForImageClassification,
										    MobileViTForSemanticSegmentation,
										    MobileViTModel,
										    MobileViTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mobilevit import (
										    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMobileViTForImageClassification,
										    TFMobileViTForSemanticSegmentation,
										    TFMobileViTModel,
										    TFMobileViTPreTrainedModel,
										)


else:
					import sys

					__lowerCAmelCase							:       Tuple          =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  []
   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_ )    ->Any:
   for i in range(encoder_config.num_hidden_layers ):
      # queries, keys and values (only weights, no biases)
      UpperCAmelCase      					=  state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight""" )

      UpperCAmelCase      					=  in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      UpperCAmelCase      					=  in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      UpperCAmelCase      					=  in_proj_weight[
          -encoder_config.hidden_size :, :
      ]
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  dct.pop(lowerCAmelCase_ )
   UpperCAmelCase      					=  val
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   if "handwritten" in checkpoint_url:
      UpperCAmelCase      					=  """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:
      UpperCAmelCase      					=  """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   UpperCAmelCase      					=  Image.open(requests.get(lowerCAmelCase_	,    stream=lowerCAmelCase_ ).raw ).convert("""RGB""" )
   return im
@torch.no_grad()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  ViTConfig(image_size=3_8_4	,    qkv_bias=lowerCAmelCase_ )
   UpperCAmelCase      					=  TrOCRConfig()

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

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

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

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

    # load state dict
   model.load_state_dict(lowerCAmelCase_ )

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

   UpperCAmelCase      					=  processor(images=prepare_img(lowerCAmelCase_ )	,    return_tensors="""pt""" ).pixel_values

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

   UpperCAmelCase      					=  torch.Size([1, 1, 5_0_2_6_5] )
   if "trocr-base-handwritten" in checkpoint_url:
      UpperCAmelCase      					=  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:
      UpperCAmelCase      					=  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:
      UpperCAmelCase      					=  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:
      UpperCAmelCase      					=  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]	,    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	 =      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)



