import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  SCREAMING_SNAKE_CASE :	Optional[int]							     =		False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor
class    A_ (       unittest.TestCase ):

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple=7     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=3     ,							__SCREAMING_SNAKE_CASE :      Tuple=18     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=30     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=4_00     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=True     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							):
   __a       =  size if size is not None else {"height": 20, "width": 20}
   __a       =  parent
   __a       =  batch_size
   __a       =  num_channels
   __a       =  image_size
   __a       =  min_resolution
   __a       =  max_resolution
   __a       =  size
   __a       =  do_normalize
   __a       =  do_convert_rgb
   __a       =  [5_12, 10_24, 20_48, 40_96]
   __a       =  patch_size if patch_size is not None else {"height": 16, "width": 16}

  def 		_UpperCAmelCase   (		self :      Any       ):
   return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
   __a       =  Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,							stream=__SCREAMING_SNAKE_CASE       ).raw       ).convert("RGB"       )
   return raw_image
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,       )
@require_torch
@require_vision
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			PixaStructImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  PixaStructImageProcessingTester(self       )

  @property
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_convert_rgb"       )       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.image_processor_tester.prepare_dummy_image()

   __a       =  self.image_processing_class(**self.image_processor_dict       )
   __a       =  20_48

   __a       =  image_processor(__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       )
   self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,							torch.tensor(0.06_06       )     ,							atol=1E-3     ,							rtol=1E-3       )       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Initialize image_processor
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

   # Test not batched input
   __a       =  (
       (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    __a       =  image_processor(
        image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

    # Test batched
    __a       =  image_processor(
        __SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)

  def 		_UpperCAmelCase   (		self :      int       ):
   # Initialize image_processor
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

   # Test not batched input
   __a       =  (
       (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
       * self.image_processor_tester.num_channels
   ) + 2

   __a       =  True

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
     __a       =  image_processor(
         image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches

    __a       =  "Hello"

    __a       =  image_processor(
        image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE     ,							header_text=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

    # Test batched
    __a       =  image_processor(
        __SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE     ,							header_text=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # Initialize image_processor
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							numpify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							np.ndarray       )

   __a       =  (
       (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    __a       =  image_processor(
        image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

    # Test batched
    __a       =  image_processor(
        __SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)

  def 		_UpperCAmelCase   (		self :      Dict       ):
   # Initialize image_processor
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							torchify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							torch.Tensor       )

   # Test not batched input
   __a       =  (
       (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
       * self.image_processor_tester.num_channels
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    __a       =  image_processor(
        image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

    # Test batched
    __a       =  image_processor(
        __SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)
@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,       )
@require_torch
@require_vision
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			PixaStructImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  PixaStructImageProcessingTester(self     ,							num_channels=4       )
   __a       =  3

  @property
  def 		_UpperCAmelCase   (		self :      int       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_convert_rgb"       )       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Initialize image_processor
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

   # Test not batched input
   __a       =  (
       (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
       * (self.image_processor_tester.num_channels - 1)
   ) + 2

   for max_patch in self.image_processor_tester.max_patches:
    # Test not batched input
    __a       =  image_processor(
        image_inputs[0]     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(1, max_patch, expected_hidden_dim)     ,							)

    # Test batched
    __a       =  image_processor(
        __SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
    self.assertEqual(
        encoded_images.shape     ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,							)



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()
SCREAMING_SNAKE_CASE :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __a       =  "backbone." if is_semantic else ""

 __a       =  []
 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   __A		(			_A							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 for i in range(config.num_hidden_layers					):
  __a       =  "backbone." if is_semantic else ""
  # queries, keys and values
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""					)

  __a       =  in_proj_weight[
      : config.hidden_size, :
  ]
  __a       =  q_bias
  __a       =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a       =  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a       =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""					)
  __a       =  state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""					)

  __a       =  gamma_a
  __a       =  gamma_a



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


 __a       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 __a       =  Image.open(requests.get(_A							,				stream=_A					).raw					)
 return im



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=False					):



 """simple docstring"""


 __a       =  False if "rvlcdip" in checkpoint_url else True
 __a       =  BeitConfig(use_absolute_position_embeddings=_A							,				use_mask_token=_A					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __a       =  1024
  __a       =  4096
  __a       =  24
  __a       =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  __a       =  16
  __a       =  "huggingface/label-files"
  __a       =  "rvlcdip-id2label.json"
  __a       =  json.load(open(hf_hub_download(_A							,				_A							,				repo_type="dataset"					)							,				"r"					)					)
  __a       =  {int(_A					): v for k, v in idalabel.items()}
  __a       =  idalabel
  __a       =  {v: k for k, v in idalabel.items()}

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

 __a       =  create_rename_keys(_A							,				has_lm_head=_A					)
 for src, dest in rename_keys:
  rename_key(_A							,				_A							,				_A					)
 read_in_q_k_v(_A							,				_A							,				has_lm_head=_A					)

 # load HuggingFace model
 __a       =  BeitForMaskedImageModeling(_A					) if has_lm_head else BeitForImageClassification(_A					)
 model.eval()
 model.load_state_dict(_A					)

 # Check outputs on an image
 __a       =  BeitImageProcessor(
     size=config.image_size							,				resample=PILImageResampling.BILINEAR							,				do_center_crop=_A					)
 __a       =  prepare_img()

 __a       =  image_processor(images=_A							,				return_tensors="pt"					)
 __a       =  encoding["pixel_values"]

 __a       =  model(_A					)
 __a       =  outputs.logits

 # verify logits
 __a       =  [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(_A					), "Shape of logits not as expected"

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

 if push_to_hub:
  if has_lm_head:
   __a       =  "dit-base" if "base" in checkpoint_url else "dit-large"
  else:
   __a       =  "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(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add image processor"							,				use_temp_dir=_A							,				)
  model.push_to_hub(
      repo_path_or_name=Path(_A							,				_A					)							,				organization="nielsr"							,				commit_message="Add model"							,				use_temp_dir=_A							,				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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""",
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


