
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					Dict						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Any:
       lowercase							:     Union[str, Any]       	=      []
       for i in range(config.num_hidden_layers				):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")				)
              rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")				)
              rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")				)
              rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")				)
              rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")				)
              rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")				)
              rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")				)
              rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")				)
              rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")				)
              rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")				)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ("""cls_token""", """deit.embeddings.cls_token"""),
               ("""dist_token""", """deit.embeddings.distillation_token"""),
               ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
               ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
               ("""pos_embed""", """deit.embeddings.position_embeddings"""),
           ]				)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("""norm.weight""", """layernorm.weight"""),
                      ("""norm.bias""", """layernorm.bias"""),
                      ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                      ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                  ]				)

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              lowercase							:     Any       	=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""				) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ("""norm.weight""", """deit.layernorm.weight"""),
                      ("""norm.bias""", """deit.layernorm.bias"""),
                      ("""head.weight""", """cls_classifier.weight"""),
                      ("""head.bias""", """cls_classifier.bias"""),
                      ("""head_dist.weight""", """distillation_classifier.weight"""),
                      ("""head_dist.bias""", """distillation_classifier.bias"""),
                  ]				)

       return rename_keys







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Tuple:
       for i in range(config.num_hidden_layers				):
              if base_model:
                     lowercase							:     Any       	=      """"""
              else:
                     lowercase							:     Dict       	=      """deit."""
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase							:     Tuple       	=      state_dict.pop(f"blocks.{i}.attn.qkv.weight"				)
              lowercase							:     Dict       	=      state_dict.pop(f"blocks.{i}.attn.qkv.bias"				)
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:     Dict       	=      in_proj_weight[
                  : config.hidden_size, :
              ]
              lowercase							:     Optional[Any]       	=      in_proj_bias[: config.hidden_size]
              lowercase							:     Union[str, Any]       	=      in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase							:     str       	=      in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase							:     str       	=      in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowercase							:     Optional[int]       	=      in_proj_bias[-config.hidden_size :]







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase							:     List[Any]       	=      dct.pop(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      val







def 						_snake_case(  ) ->    str:
       lowercase							:     List[Any]       	=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:     str       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Tuple       	=      DeiTConfig()
       # all deit models have fine-tuned heads
       lowercase							:     int       	=      False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowercase							:     Optional[Any]       	=      1_000
       lowercase							:     Any       	=      """huggingface/label-files"""
       lowercase							:     List[str]       	=      """imagenet-1k-id2label.json"""
       lowercase							:     Optional[Any]       	=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""				)					,	"""r"""				)				)
       lowercase							:     Tuple       	=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
       lowercase							:     Any       	=      idalabel
       lowercase							:     str       	=      {v: k for k, v in idalabel.items()}
       lowercase							:     List[Any]       	=      int(deit_name[-6:-4]				)
       lowercase							:     Optional[Any]       	=      int(deit_name[-3:]				)
       # size of the architecture
       if deit_name[9:].startswith("""tiny"""				):
              lowercase							:     Union[str, Any]       	=      192
              lowercase							:     List[Any]       	=      768
              lowercase							:     List[Any]       	=      12
              lowercase							:     Union[str, Any]       	=      3
       elif deit_name[9:].startswith("""small"""				):
              lowercase							:     Optional[int]       	=      384
              lowercase							:     str       	=      1_536
              lowercase							:     Optional[int]       	=      12
              lowercase							:     Tuple       	=      6
       if deit_name[9:].startswith("""base"""				):
              pass
       elif deit_name[4:].startswith("""large"""				):
              lowercase							:     List[str]       	=      1_024
              lowercase							:     List[str]       	=      4_096
              lowercase							:     List[str]       	=      24
              lowercase							:     List[str]       	=      16

       # load original model from timm
       lowercase							:     List[str]       	=      timm.create_model(SCREAMING_SNAKE_CASE__					,	pretrained=SCREAMING_SNAKE_CASE__				)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowercase							:     Optional[int]       	=      timm_model.state_dict()
       lowercase							:     List[Any]       	=      create_rename_keys(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       for src, dest in rename_keys:
              rename_key(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       read_in_q_k_v(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       # load HuggingFace model
       lowercase							:     Tuple       	=      DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__				).eval()
       model.load_state_dict(SCREAMING_SNAKE_CASE__				)

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowercase							:     Optional[int]       	=      int(
           (256 / 224) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowercase							:     Optional[Any]       	=      DeiTImageProcessor(size=SCREAMING_SNAKE_CASE__					,	crop_size=config.image_size				)
       lowercase							:     List[str]       	=      image_processor(images=prepare_img()					,	return_tensors="""pt"""				)
       lowercase							:     int       	=      encoding["""pixel_values"""]
       lowercase							:     List[Any]       	=      model(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      timm_model(SCREAMING_SNAKE_CASE__				)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(SCREAMING_SNAKE_CASE__					,	outputs.logits					,	atol=1e-3				)

       Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
       print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       print(f"Saving image processor to {pytorch_dump_folder_path}"				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Union[str, Any]						    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--deit_name""",
      default="""vit_deit_base_distilled_patch16_224""",
      type=str,
      help="""Name of the DeiT timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  lowercase  :					Optional[Any]						    =							parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







import unittest

from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST



class 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=7       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=99       ,							A_=24       ,							A_=2       ,							A_=6       ,							A_=37       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=16       ,							A_=2       ,							A_=0.02       ,							A_=3       ,							A_=None       ,							A_=1000       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   parent
       UpperCamelCase	:					List[Any]       	=   batch_size
       UpperCamelCase	:					Dict       	=   seq_length
       UpperCamelCase	:					Tuple       	=   is_training
       UpperCamelCase	:					Union[str, Any]       	=   use_input_mask
       UpperCamelCase	:					Tuple       	=   use_token_type_ids
       UpperCamelCase	:					Optional[Any]       	=   use_labels
       UpperCamelCase	:					str       	=   vocab_size
       UpperCamelCase	:					Optional[int]       	=   hidden_size
       UpperCamelCase	:					Any       	=   num_hidden_layers
       UpperCamelCase	:					Optional[Any]       	=   num_attention_heads
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Optional[Any]       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   hidden_dropout_prob
       UpperCamelCase	:					Union[str, Any]       	=   attention_probs_dropout_prob
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					str       	=   type_vocab_size
       UpperCamelCase	:					Optional[int]       	=   type_sequence_label_size
       UpperCamelCase	:					Dict       	=   initializer_range
       UpperCamelCase	:					int       	=   num_labels
       UpperCamelCase	:					Optional[int]       	=   scope
       UpperCamelCase	:					int       	=   range_bbox





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size   )

       UpperCamelCase	:					Any       	=   ids_tensor([self.batch_size, self.seq_length, 4]       ,							self.range_bbox   )
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]   ):
        for j in range(bbox.shape[1]   ):
         if bbox[i, j, 3] < bbox[i, j, 1]:
          UpperCamelCase	:					Union[str, Any]       	=   bbox[i, j, 3]
          UpperCamelCase	:					int       	=   bbox[i, j, 1]
          UpperCamelCase	:					int       	=   t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          UpperCamelCase	:					List[str]       	=   bbox[i, j, 2]
          UpperCamelCase	:					Optional[int]       	=   bbox[i, j, 0]
          UpperCamelCase	:					Optional[Any]       	=   t

       UpperCamelCase	:					Dict       	=   None
       if self.use_input_mask:
        UpperCamelCase	:					Optional[int]       	=   ids_tensor([self.batch_size, self.seq_length]       ,							vocab_size=2   )

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

       UpperCamelCase	:					Dict       	=   None
       UpperCamelCase	:					int       	=   None
       if self.use_labels:
        UpperCamelCase	:					List[Any]       	=   ids_tensor([self.batch_size]       ,							self.type_sequence_label_size   )
        UpperCamelCase	:					int       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels   )

       UpperCamelCase	:					List[Any]       	=   self.get_config()

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return LiltConfig(
           vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							type_vocab_size=self.type_vocab_size       ,							initializer_range=self.initializer_range       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   LiltModel(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					str       	=   model(A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_   )
       UpperCamelCase	:					Optional[int]       	=   model(A_       ,							bbox=A_       ,							token_type_ids=A_   )
       UpperCamelCase	:					Any       	=   model(A_       ,							bbox=A_   )

       self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)   )
       self.parent.assertEqual(result.pooler_output.shape       ,							(self.batch_size, self.hidden_size)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.num_labels
       UpperCamelCase	:					Dict       	=   LiltForTokenClassification(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Dict       	=   model(
           A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							labels=A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   LiltForQuestionAnswering(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					List[str]       	=   model(
           A_       ,							bbox=A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							start_positions=A_       ,							end_positions=A_       ,							)
       self.parent.assertEqual(result.start_logits.shape       ,							(self.batch_size, self.seq_length)   )
       self.parent.assertEqual(result.end_logits.shape       ,							(self.batch_size, self.seq_length)   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    
       )	:					Tuple       	=   config_and_inputs
       UpperCamelCase	:					Tuple       	=   {
           "input_ids": input_ids,
           "bbox": bbox,
           "token_type_ids": token_type_ids,
           "attention_mask": input_mask,
       }
       return config, inputs_dict



@require_torch
class 					A__   (       __snake_case       ,   __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Union[str, Any]						    =   (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase	:Optional[Any]						    =   (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase	:Dict						    =   False
      _UpperCAmelCase	:Union[str, Any]						    =   False





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       return True





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   LiltModelTester(self   )
       UpperCamelCase	:					Optional[int]       	=   ConfigTester(self       ,							config_class=A_       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCamelCase	:					Union[str, Any]       	=   type
        self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase	:					Dict       	=   LiltModel.from_pretrained(A_   )
        self.assertIsNotNone(A_   )



@require_torch
@slow
class 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"   ).to(A_   )

       UpperCamelCase	:					Tuple       	=   torch.tensor([[1, 2]]       ,							device=A_   )
       UpperCamelCase	:					List[str]       	=   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]       ,							device=A_   )

       # forward pass
       with torch.no_grad():
        UpperCamelCase	:					Optional[int]       	=   model(input_ids=A_       ,							bbox=A_   )

       UpperCamelCase	:					List[str]       	=   torch.Size([1, 2, 768]   )
       UpperCamelCase	:					Any       	=   torch.tensor(
           [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]       ,							device=A_       ,							)

       self.assertTrue(outputs.last_hidden_state.shape       ,							A_   )
       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]       ,							A_       ,							atol=1e-3   )   )
