


import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roformer.modeling_flax_roformer import (
       FlaxRoFormerForMaskedLM,
       FlaxRoFormerForMultipleChoice,
       FlaxRoFormerForQuestionAnswering,
       FlaxRoFormerForSequenceClassification,
       FlaxRoFormerForTokenClassification,
       FlaxRoFormerModel,
   )
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=13						,				__A=7						,				__A=True						,				__A=True						,				__A=True						,				__A=True						,				__A=99						,				__A=32						,				__A=5						,				__A=4						,				__A=37						,				__A="gelu"						,				__A=0.1						,				__A=0.1						,				__A=512						,				__A=16						,				__A=2						,				__A=0.02						,				__A=4						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				parent
     lowerCamelCase :					Dict    =				batch_size
     lowerCamelCase :					Tuple    =				seq_length
     lowerCamelCase :					List[Any]    =				is_training
     lowerCamelCase :					Optional[int]    =				use_attention_mask
     lowerCamelCase :					Optional[int]    =				use_token_type_ids
     lowerCamelCase :					Union[str, Any]    =				use_labels
     lowerCamelCase :					Optional[int]    =				vocab_size
     lowerCamelCase :					str    =				hidden_size
     lowerCamelCase :					Union[str, Any]    =				num_hidden_layers
     lowerCamelCase :					List[Any]    =				num_attention_heads
     lowerCamelCase :					Tuple    =				intermediate_size
     lowerCamelCase :					List[Any]    =				hidden_act
     lowerCamelCase :					Tuple    =				hidden_dropout_prob
     lowerCamelCase :					Union[str, Any]    =				attention_probs_dropout_prob
     lowerCamelCase :					Any    =				max_position_embeddings
     lowerCamelCase :					Optional[Any]    =				type_vocab_size
     lowerCamelCase :					Dict    =				type_sequence_label_size
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Tuple    =				num_choices






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size )

     lowerCamelCase :					Optional[Any]    =				None
     if self.use_attention_mask:
       lowerCamelCase :					Optional[Any]    =				random_attention_mask([self.batch_size, self.seq_length] )

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

     lowerCamelCase :					List[str]    =				RoFormerConfig(
         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						,				is_decoder=__A						,				initializer_range=self.initializer_range						,				)

     return config, input_ids, token_type_ids, attention_mask






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.prepare_config_and_inputs()
     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					str    =				config_and_inputs
     lowerCamelCase :					Optional[int]    =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
     return config, inputs_dict



@require_flax
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Optional[Any]     		=		True

   __A :      Dict     		=		(
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				FlaxRoFormerModelTester(self )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for model_class_name in self.all_model_classes:
       lowerCamelCase :					str    =				model_class_name.from_pretrained("junnyu/roformer_chinese_small"						,				from_pt=__A )
       lowerCamelCase :					Union[str, Any]    =				model(np.ones((1, 1) ) )
       self.assertIsNotNone(__A )



@require_flax
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
     lowerCamelCase :					List[str]    =				jnp.array([[0, 1, 2, 3, 4, 5]] )
     lowerCamelCase :					str    =				model(__A )[0]

     lowerCamelCase :					Union[str, Any]    =				5_0000

     lowerCamelCase :					int    =				(1, 6, vocab_size)
     self.assertEqual(output.shape						,				__A )

     lowerCamelCase :					Optional[Any]    =				jnp.array(
         [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )

     self.assertTrue(jnp.allclose(output[:, :3, :3]						,				__A						,				atol=1e-4 ) )







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()
_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				"backbone." if is_semantic else ""

  lowerCamelCase :					int    =				[]
  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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

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

    lowerCamelCase :					str    =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase :					Any    =				q_bias
    lowerCamelCase :					Any    =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase :					Optional[int]    =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase :					int    =				v_bias

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

    lowerCamelCase :					int    =				gamma_a
    lowerCamelCase :					Optional[Any]    =				gamma_a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				val






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  return im






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				False if "rvlcdip" in checkpoint_url else True
  lowerCamelCase :					str    =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_      ,						use_mask_token=SCREAMING_SNAKE_CASE_	)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    lowerCamelCase :					Union[str, Any]    =				1024
    lowerCamelCase :					Any    =				4096
    lowerCamelCase :					str    =				24
    lowerCamelCase :					List[Any]    =				16

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

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

  lowerCamelCase :					Tuple    =				create_rename_keys(SCREAMING_SNAKE_CASE_      ,						has_lm_head=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_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)

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

  # Check outputs on an image
  lowerCamelCase :					str    =				BeitImageProcessor(
      size=config.image_size      ,						resample=PILImageResampling.BILINEAR      ,						do_center_crop=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				prepare_img()

  lowerCamelCase :					Optional[int]    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Optional[Any]    =				encoding["pixel_values"]

  lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				outputs.logits

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

  Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
  print(f"""Saving model 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 push_to_hub:
    if has_lm_head:
      lowerCamelCase :					Optional[Any]    =				"dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      lowerCamelCase :					Dict    =				"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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add image processor"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)
    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)


if __name__ == "__main__":
   _snake_case             =     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''',
   )
   _snake_case             =     parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



