



"""simple docstring"""
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=13      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=4      ,     _SCREAMING_SNAKE_CASE=37      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=10      ,     _SCREAMING_SNAKE_CASE=0.02      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	parent
    UpperCAmelCase	:					List[Any]	=	batch_size
    UpperCAmelCase	:					Optional[Any]	=	image_size
    UpperCAmelCase	:					int	=	patch_size
    UpperCAmelCase	:					Optional[Any]	=	num_channels
    UpperCAmelCase	:					Optional[int]	=	is_training
    UpperCAmelCase	:					Union[str, Any]	=	use_labels
    UpperCAmelCase	:					Tuple	=	hidden_size
    UpperCAmelCase	:					List[Any]	=	num_hidden_layers
    UpperCAmelCase	:					List[Any]	=	num_attention_heads
    UpperCAmelCase	:					Dict	=	intermediate_size
    UpperCAmelCase	:					Optional[int]	=	hidden_act
    UpperCAmelCase	:					List[Any]	=	hidden_dropout_prob
    UpperCAmelCase	:					str	=	attention_probs_dropout_prob
    UpperCAmelCase	:					List[str]	=	type_sequence_label_size
    UpperCAmelCase	:					List[str]	=	initializer_range

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase	:					Union[str, Any]	=	(image_size // patch_size) ** 2
    UpperCAmelCase	:					Optional[int]	=	num_patches + 1

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

    UpperCAmelCase	:					Tuple	=	ViTConfig(
        image_size=self.image_size      ,     patch_size=self.patch_size      ,     num_channels=self.num_channels      ,     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      ,     is_decoder=_SCREAMING_SNAKE_CASE      ,     initializer_range=self.initializer_range      ,     )

    return config, pixel_values

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	FlaxViTModel(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase	:					Any	=	(self.image_size, self.image_size)
    UpperCAmelCase	:					Optional[int]	=	(self.patch_size, self.patch_size)
    UpperCAmelCase	:					Optional[int]	=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, num_patches + 1, self.hidden_size)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	self.type_sequence_label_size
    UpperCAmelCase	:					Dict	=	FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.type_sequence_label_size)						)

    # test greyscale images
    UpperCAmelCase	:					Dict	=	1
    UpperCAmelCase	:					Optional[Any]	=	FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
    UpperCAmelCase	:					str	=	model(_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	self.prepare_config_and_inputs()
    (
        UpperCAmelCase						
    )	:					List[Any]	=	config_and_inputs
    UpperCAmelCase	:					str	=	{"pixel_values": pixel_values}
    return config, inputs_dict



@require_flax
class       SCREAMING_SNAKE_CASE__  (					__lowercase      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	FlaxViTModelTester(self						)
    UpperCAmelCase	:					Any	=	ConfigTester(self      ,     config_class=_SCREAMING_SNAKE_CASE      ,     has_text_modality=_SCREAMING_SNAKE_CASE      ,     hidden_size=37						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					List[Any]	=	model_class(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[int]	=	inspect.signature(model.__call__						)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase	:					Dict	=	[*signature.parameters.keys()]

     UpperCAmelCase	:					Any	=	["pixel_values"]
     self.assertListEqual(arg_names[:1]      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__						):
      UpperCAmelCase	:					List[Any]	=	self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					List[Any]	=	model_class(_SCREAMING_SNAKE_CASE						)

      @jax.jit
      def model_jitted(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						):
       return model(pixel_values=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

      with self.subTest("""JIT Enabled"""						):
       UpperCAmelCase	:					int	=	model_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

      with self.subTest("""JIT Disabled"""						):
       with jax.disable_jit():
        UpperCAmelCase	:					Optional[Any]	=	model_jitted(**_SCREAMING_SNAKE_CASE						).to_tuple()

      self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     len(_SCREAMING_SNAKE_CASE						)						)
      for jitted_output, output in zip(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
       self.assertEqual(jitted_output.shape      ,     output.shape						)




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    for model_class_name in self.all_model_classes:
     UpperCAmelCase	:					List[Any]	=	model_class_name.from_pretrained("""google/vit-base-patch16-224"""						)
     UpperCAmelCase	:					Optional[int]	=	model(np.ones((1, 3, 224, 224)						)						)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)


import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

							__UpperCamelCase	:   Optional[Any]			   =					[]
							for i in range(config.num_hidden_layers):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias'))
														rename_keys.append(
														    (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight'))
														rename_keys.append(
														    (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight'))
														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias'))

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F'{prefix}cls_token', "beit.embeddings.cls_token"),
							        (F'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
							        (F'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
							        (F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
							    ])

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", "beit.embeddings.mask_token"),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.bias"),
														    ])
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
														        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
														        ("head.weight", "classifier.weight"),
														        ("head.bias", "classifier.bias"),
														    ])

							return rename_keys




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





							for i in range(config.num_hidden_layers):
														__UpperCamelCase	:   List[Any]			   =					"backbone." if is_semantic else ""
														# queries, keys and values
														__UpperCamelCase	:   Tuple			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight')
														__UpperCamelCase	:   Optional[Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias')
														__UpperCamelCase	:   int			   =					state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias')

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					BeitConfig(use_absolute_position_embeddings=_lowerCamelCase						,  use_mask_token=_lowerCamelCase)

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

							# labels
							if "rvlcdip" in checkpoint_url:
														__UpperCamelCase	:   List[str]			   =					16
														__UpperCamelCase	:   str			   =					"huggingface/label-files"
														__UpperCamelCase	:   str			   =					"rvlcdip-id2label.json"
														__UpperCamelCase	:   Tuple			   =					json.load(open(hf_hub_download(_lowerCamelCase						,  _lowerCamelCase						,  repo_type="dataset")						,  "r"))
														__UpperCamelCase	:   Dict			   =					{int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase	:   Optional[Any]			   =					idalabel
														__UpperCamelCase	:   List[Any]			   =					{v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							__UpperCamelCase	:   str			   =					torch.hub.load_state_dict_from_url(_lowerCamelCase						,  map_location="cpu")["model"]

							__UpperCamelCase	:   Optional[Any]			   =					create_rename_keys(_lowerCamelCase						,  has_lm_head=_lowerCamelCase)
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							read_in_q_k_v(_lowerCamelCase						,  _lowerCamelCase						,  has_lm_head=_lowerCamelCase)

							# load HuggingFace model
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(_lowerCamelCase), "Shape 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 image processor to {pytorch_dump_folder_path}')
							image_processor.save_pretrained(_lowerCamelCase)

							if push_to_hub:
														if has_lm_head:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[Any]			   =					"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add image processor"						,  use_temp_dir=_lowerCamelCase						,  )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,  _lowerCamelCase)						,  organization="nielsr"						,  commit_message="Add model"						,  use_temp_dir=_lowerCamelCase						,  )


if __name__ == "__main__":
		lowercase  :					Optional[Any]													=       argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
		    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.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		lowercase  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)