'''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()
lowerCAmelCase_ : int									=      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      []
      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 __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str]				)	->							Any:
      '''simple docstring'''


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

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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[int]				)	->							str:
      '''simple docstring'''


      lowercase_     =      dct.pop(__lowerCamelCase				)
      lowercase_     =      val


def __a (	__lowerCamelCase			:						int				)	->							Dict:
      '''simple docstring'''


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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						List[str]				)	->							str:
      '''simple docstring'''


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

      # size of the architecture
      if "base" in checkpoint_url:
            lowercase_     =      768
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            lowercase_     =      1_024
            lowercase_     =      4_096
            lowercase_     =      24
            lowercase_     =      16
            lowercase_     =      1_024
      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:
            lowercase_     =      False
            lowercase_     =      "relu"
            lowercase_     =      1_024
            lowercase_     =      True
            lowercase_     =      False
            lowercase_     =      False

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

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

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

    # load state dict
      model.load_state_dict(__lowerCamelCase				)

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

      lowercase_     =      processor(images=prepare_img(__lowerCamelCase				) ,		return_tensors="pt"				).pixel_values

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

      lowercase_     =      torch.Size([1, 1, 50_265]				)
      if "trocr-base-handwritten" in checkpoint_url:
            lowercase_     =      torch.tensor(
                [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]				)
      elif "trocr-large-handwritten" in checkpoint_url:
            lowercase_     =      torch.tensor(
                [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]				)
      elif "trocr-base-printed" in checkpoint_url:
            lowercase_     =      torch.tensor(
                [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]				)
      elif "trocr-large-printed" in checkpoint_url:
            lowercase_     =      torch.tensor(
                [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]				)

      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__":
       lowerCAmelCase_ : Union[str, 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_ : Any									=      parser.parse_args()
       convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           CTRLForSequenceClassification,
           CTRLLMHeadModel,
           CTRLModel,
       )





class 							lowercase						:


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any=14   ,		__lowerCAmelCase				:	Optional[Any]=7   ,		__lowerCAmelCase				:	List[str]=True   ,		__lowerCAmelCase				:	Optional[int]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Any=99   ,		__lowerCAmelCase				:	Tuple=32   ,		__lowerCAmelCase				:	Union[str, Any]=5   ,		__lowerCAmelCase				:	str=4   ,		__lowerCAmelCase				:	Dict=37   ,		__lowerCAmelCase				:	Optional[int]="gelu"   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	Tuple=0.1   ,		__lowerCAmelCase				:	str=512   ,		__lowerCAmelCase				:	Optional[Any]=16   ,		__lowerCAmelCase				:	Optional[int]=2   ,		__lowerCAmelCase				:	str=0.02   ,		__lowerCAmelCase				:	str=3   ,		__lowerCAmelCase				:	Tuple=4   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		)      ->					List[Any]:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      seq_length
         lowercase_     =      is_training
         lowercase_     =      use_token_type_ids
         lowercase_     =      use_input_mask
         lowercase_     =      use_labels
         lowercase_     =      use_mc_token_ids
         lowercase_     =      vocab_size
         lowercase_     =      hidden_size
         lowercase_     =      num_hidden_layers
         lowercase_     =      num_attention_heads
         lowercase_     =      intermediate_size
         lowercase_     =      hidden_act
         lowercase_     =      hidden_dropout_prob
         lowercase_     =      attention_probs_dropout_prob
         lowercase_     =      max_position_embeddings
         lowercase_     =      type_vocab_size
         lowercase_     =      type_sequence_label_size
         lowercase_     =      initializer_range
         lowercase_     =      num_labels
         lowercase_     =      num_choices
         lowercase_     =      scope
         lowercase_     =      self.vocab_size - 1


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size)

         lowercase_     =      None
         if self.use_input_mask:
               lowercase_     =      random_attention_mask([self.batch_size, self.seq_length])

         lowercase_     =      None
         if self.use_token_type_ids:
               lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.type_vocab_size)

         lowercase_     =      None
         if self.use_mc_token_ids:
               lowercase_     =      ids_tensor([self.batch_size, self.num_choices]   ,		self.seq_length)

         lowercase_     =      None
         lowercase_     =      None
         lowercase_     =      None
         if self.use_labels:
               lowercase_     =      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size)
               lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.num_labels)
               lowercase_     =      ids_tensor([self.batch_size]   ,		self.num_choices)

         lowercase_     =      self.get_config()

         lowercase_     =      ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,		2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         return CTRLConfig(
             vocab_size=self.vocab_size   ,		n_embd=self.hidden_size   ,		n_layer=self.num_hidden_layers   ,		n_head=self.num_attention_heads   ,		n_positions=self.max_position_embeddings   ,		pad_token_id=self.pad_token_id   ,		)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Union[str, Any]   ,		*__lowerCAmelCase				:	Tuple)      ->					List[str]:
         lowercase_     =      CTRLModel(config=__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		head_mask=__lowerCAmelCase)
         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase)
         lowercase_     =      model(__lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(len(result.past_key_values)   ,		config.n_layer)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Dict   ,		*__lowerCAmelCase				:	int)      ->					int:
         lowercase_     =      CTRLLMHeadModel(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

         lowercase_     =      model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         self.parent.assertEqual(result.loss.shape   ,		())
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.vocab_size))


   def __UpperCAmelCase       (   self				:	Dict)      ->					int:
         lowercase_     =      self.prepare_config_and_inputs()

         (
             (
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							
         )     =      config_and_inputs

         lowercase_     =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

         return config, inputs_dict


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	List[str]   ,		*__lowerCAmelCase				:	List[Any])      ->					int:
         lowercase_     =      self.num_labels
         lowercase_     =      CTRLForSequenceClassification(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size)
         lowercase_     =      model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels))





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   lowerCamelCase_    					=(CTRLLMHeadModel,) if is_torch_available() else ()
   lowerCamelCase_    					=(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_    					=True
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	int)      ->					Optional[int]:
         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
               # config could not be created.
               return True

         return False


   def __UpperCAmelCase       (   self				:	str)      ->					Tuple:
         lowercase_     =      CTRLModelTester(self)
         lowercase_     =      ConfigTester(self   ,		config_class=__lowerCAmelCase   ,		n_embd=37)


   def __UpperCAmelCase       (   self				:	Dict)      ->					Optional[Any]:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase       (   self				:	str)      ->					List[Any]:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         pass


   @slow
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     =      CTRLModel.from_pretrained(__lowerCAmelCase)
               self.assertIsNotNone(__lowerCAmelCase)


   @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Dict:
         pass





@require_torch
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Any:
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
         gc.collect()
         torch.cuda.empty_cache()


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Any:
         lowercase_     =      CTRLLMHeadModel.from_pretrained("ctrl")
         model.to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(
             [[1_1859, 0, 1611, 8]]   ,		dtype=torch.long   ,		device=__lowerCAmelCase)  # Legal the president is
         lowercase_     =      [
             1_1859,
             0,
             1611,
             8,
             5,
             150,
             2_6449,
             2,
             19,
             348,
             469,
             3,
             2595,
             48,
             2_0740,
             24_6533,
             24_6533,
             19,
             30,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         lowercase_     =      model.generate(__lowerCAmelCase   ,		do_sample=__lowerCAmelCase)
         self.assertListEqual(output_ids[0].tolist()   ,		__lowerCAmelCase)






