



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__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Dict:
			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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[Any]:
			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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							)    ->   Dict:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False       )    ->   str:
			A__	  =     False if 'rvlcdip' in checkpoint_url else True
			A__	  =     BeitConfig(use_absolute_position_embeddings=__UpperCamelCase			,						use_mask_token=__UpperCamelCase       )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						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(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): 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(__UpperCamelCase			,						map_location='cpu'       )['model']

			A__	  =     create_rename_keys(__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     BeitForMaskedImageModeling(__UpperCamelCase       ) if has_lm_head else BeitForImageClassification(__UpperCamelCase       )
			model.eval()
			model.load_state_dict(__UpperCamelCase       )

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

			A__	  =     image_processor(images=__UpperCamelCase			,						return_tensors='pt'       )
			A__	  =     encoding['pixel_values']

			A__	  =     model(__UpperCamelCase       )
			A__	  =     outputs.logits

			# verify logits
			A__	  =     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(__UpperCamelCase       ), "Shape of logits not as expected"

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

			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(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add image processor'			,						use_temp_dir=__UpperCamelCase			,						)
						model.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add model'			,						use_temp_dir=__UpperCamelCase			,						)


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









from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
							import tensorflow as tf

							from transformers import TFBlipTextModel
							from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Optional[Any]=12					,       _snake_case       :							Any=7					,       _snake_case       :							List[str]=True					,       _snake_case       :							int=True					,       _snake_case       :							int=True					,       _snake_case       :							Tuple=99					,       _snake_case       :							List[Any]=32					,       _snake_case       :							Optional[int]=32					,       _snake_case       :							List[str]=2					,       _snake_case       :							List[str]=4					,       _snake_case       :							List[Any]=37					,       _snake_case       :							Union[str, Any]=0.1					,       _snake_case       :							Tuple=0.1					,       _snake_case       :							Dict=5_12					,       _snake_case       :							Union[str, Any]=0.02					,       _snake_case       :							Any=0					,       _snake_case       :							Optional[Any]=None					,       ):






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     seq_length
								A__	  =     is_training
								A__	  =     use_input_mask
								A__	  =     use_labels
								A__	  =     vocab_size
								A__	  =     hidden_size
								A__	  =     projection_dim
								A__	  =     num_hidden_layers
								A__	  =     num_attention_heads
								A__	  =     intermediate_size
								A__	  =     dropout
								A__	  =     attention_dropout
								A__	  =     max_position_embeddings
								A__	  =     initializer_range
								A__	  =     scope
								A__	  =     bos_token_id



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     ids_tensor([self.batch_size, self.seq_length]					,       self.vocab_size       )

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

								if input_mask is not None:
											A__	  =     input_mask.numpy()
											A__  ,			A__	  =     input_mask.shape
											A__	  =     np.random.randint(1					,       seq_length - 1					,       size=(batch_size,)       )
											for batch_idx, start_index in enumerate(_snake_case       ):
														A__	  =     1
														A__	  =     0

								A__	  =     self.get_config()

								return config, input_ids, tf.convert_to_tensor(_snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								return BlipTextConfig(
								    vocab_size=self.vocab_size					,       hidden_size=self.hidden_size					,       projection_dim=self.projection_dim					,       num_hidden_layers=self.num_hidden_layers					,       num_attention_heads=self.num_attention_heads					,       intermediate_size=self.intermediate_size					,       dropout=self.dropout					,       attention_dropout=self.attention_dropout					,       max_position_embeddings=self.max_position_embeddings					,       initializer_range=self.initializer_range					,       bos_token_id=self.bos_token_id					,       )



					def      _a				(  self       :							int					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Any					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     TFBlipTextModel(config=_snake_case       )
								A__	  =     model(_snake_case					,       attention_mask=_snake_case					,       training=_snake_case       )
								A__	  =     model(_snake_case					,       training=_snake_case       )
								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      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     self.prepare_config_and_inputs()
								A__  ,			A__  ,			A__	  =     config_and_inputs
								A__	  =     {'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict







@require_tf
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Tuple      							=     (TFBlipTextModel,) if is_tf_available() else ()
					A__					:		Optional[int]      							=     False
					A__					:		Union[str, Any]      							=     False
					A__					:		Union[str, Any]      							=     False



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     BlipTextModelTester(self       )
								A__	  =     ConfigTester(self					,       config_class=_snake_case					,       hidden_size=37       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='Blip does not use inputs_embeds'       )
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'       )
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'       )
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								pass



					@slow
					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__	  =     TFBlipTextModel.from_pretrained(_snake_case       )
											self.assertIsNotNone(_snake_case       )



					def      _a				(  self       :							int					,       _snake_case       :							int=True       ):






								"""simple docstring"""


								super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case       )




