




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger(__name__)







def 		_A(       UpperCamelCase__				)    -> Union[str, Any]:
							'''simple docstring'''


							__lowercase      =    OrderedDict()
							for key, value in state_dict.items():
														if key.startswith('''module.encoder'''				):
																					__lowercase      =    key.replace('''module.encoder'''				,       '''glpn.encoder'''				)
														if key.startswith('''module.decoder'''				):
																					__lowercase      =    key.replace('''module.decoder'''				,       '''decoder.stages'''				)
														if "patch_embed" in key:
																					# replace for example patch_embed1 by patch_embeddings.0
																					__lowercase      =    key[key.find('''patch_embed'''				) + len('''patch_embed'''				)]
																					__lowercase      =    key.replace(F'patch_embed{idx}'				,       F'patch_embeddings.{int(UpperCamelCase__				)-1}'				)
														if "norm" in key:
																					__lowercase      =    key.replace('''norm'''				,       '''layer_norm'''				)
														if "glpn.encoder.layer_norm" in key:
																					# replace for example layer_norm1 by layer_norm.0
																					__lowercase      =    key[key.find('''glpn.encoder.layer_norm'''				) + len('''glpn.encoder.layer_norm'''				)]
																					__lowercase      =    key.replace(F'layer_norm{idx}'				,       F'layer_norm.{int(UpperCamelCase__				)-1}'				)
														if "layer_norm1" in key:
																					__lowercase      =    key.replace('''layer_norm1'''				,       '''layer_norm_1'''				)
														if "layer_norm2" in key:
																					__lowercase      =    key.replace('''layer_norm2'''				,       '''layer_norm_2'''				)
														if "block" in key:
																					# replace for example block1 by block.0
																					__lowercase      =    key[key.find('''block'''				) + len('''block'''				)]
																					__lowercase      =    key.replace(F'block{idx}'				,       F'block.{int(UpperCamelCase__				)-1}'				)
														if "attn.q" in key:
																					__lowercase      =    key.replace('''attn.q'''				,       '''attention.self.query'''				)
														if "attn.proj" in key:
																					__lowercase      =    key.replace('''attn.proj'''				,       '''attention.output.dense'''				)
														if "attn" in key:
																					__lowercase      =    key.replace('''attn'''				,       '''attention.self'''				)
														if "fc1" in key:
																					__lowercase      =    key.replace('''fc1'''				,       '''dense1'''				)
														if "fc2" in key:
																					__lowercase      =    key.replace('''fc2'''				,       '''dense2'''				)
														if "linear_pred" in key:
																					__lowercase      =    key.replace('''linear_pred'''				,       '''classifier'''				)
														if "linear_fuse" in key:
																					__lowercase      =    key.replace('''linear_fuse.conv'''				,       '''linear_fuse'''				)
																					__lowercase      =    key.replace('''linear_fuse.bn'''				,       '''batch_norm'''				)
														if "linear_c" in key:
																					# replace for example linear_c4 by linear_c.3
																					__lowercase      =    key[key.find('''linear_c'''				) + len('''linear_c'''				)]
																					__lowercase      =    key.replace(F'linear_c{idx}'				,       F'linear_c.{int(UpperCamelCase__				)-1}'				)
														if "bot_conv" in key:
																					__lowercase      =    key.replace('''bot_conv'''				,       '''0.convolution'''				)
														if "skip_conv1" in key:
																					__lowercase      =    key.replace('''skip_conv1'''				,       '''1.convolution'''				)
														if "skip_conv2" in key:
																					__lowercase      =    key.replace('''skip_conv2'''				,       '''2.convolution'''				)
														if "fusion1" in key:
																					__lowercase      =    key.replace('''fusion1'''				,       '''1.fusion'''				)
														if "fusion2" in key:
																					__lowercase      =    key.replace('''fusion2'''				,       '''2.fusion'''				)
														if "fusion3" in key:
																					__lowercase      =    key.replace('''fusion3'''				,       '''3.fusion'''				)
														if "fusion" in key and "conv" in key:
																					__lowercase      =    key.replace('''conv'''				,       '''convolutional_layer'''				)
														if key.startswith('''module.last_layer_depth'''				):
																					__lowercase      =    key.replace('''module.last_layer_depth'''				,       '''head.head'''				)
														__lowercase      =    value

							return new_state_dict







def 		_A(       UpperCamelCase__				,       UpperCamelCase__				)    -> Dict:
							'''simple docstring'''


							for i in range(config.num_encoder_blocks				):
														for j in range(config.depths[i]				):
																					# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																					__lowercase      =    state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
																					__lowercase      =    state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'				)
																					# next, add keys and values (in that order) to the state dict
																					__lowercase      =    kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__lowercase      =    kv_bias[: config.hidden_sizes[i]]
																					__lowercase      =    kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__lowercase      =    kv_bias[config.hidden_sizes[i] :]







def 		_A(       )    -> Optional[Any]:
							'''simple docstring'''


							__lowercase      =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowercase      =    Image.open(requests.get(UpperCamelCase__				,       stream=UpperCamelCase__				).raw				)

							return image







@torch.no_grad()
def 		_A(       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False				,       UpperCamelCase__=None				)    -> str:
							'''simple docstring'''


							__lowercase      =    GLPNConfig(hidden_sizes=[64, 128, 320, 512]				,       decoder_hidden_size=64				,       depths=[3, 8, 27, 3]				)

							# load image processor (only resize + rescale)
							__lowercase      =    GLPNImageProcessor()

							# prepare image
							__lowercase      =    prepare_img()
							__lowercase      =    image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''				).pixel_values

							logger.info('''Converting model...'''				)

							# load original state dict
							__lowercase      =    torch.load(UpperCamelCase__				,       map_location=torch.device('''cpu'''				)				)

							# rename keys
							__lowercase      =    rename_keys(UpperCamelCase__				)

							# key and value matrices need special treatment
							read_in_k_v(UpperCamelCase__				,       UpperCamelCase__				)

							# create HuggingFace model and load state dict
							__lowercase      =    GLPNForDepthEstimation(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)
							model.eval()

							# forward pass
							__lowercase      =    model(UpperCamelCase__				)
							__lowercase      =    outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					__lowercase      =    torch.tensor(
																					    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]				)
														elif "kitti" in model_name:
																					__lowercase      =    torch.tensor(
																					    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				)
														else:
																					raise ValueError(F'Unknown model name: {model_name}'				)

														__lowercase      =    torch.Size([1, 480, 640]				)

														assert predicted_depth.shape == expected_shape
														assert torch.allclose(predicted_depth[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4				)
														print('''Looks ok!'''				)

							# finally, push to hub if required
							if push_to_hub:
														logger.info('''Pushing model and image processor to the hub...'''				)
														model.push_to_hub(
														    repo_path_or_name=Path(UpperCamelCase__				,       UpperCamelCase__				)				,       organization='''nielsr'''				,       commit_message='''Add model'''				,       use_temp_dir=UpperCamelCase__				,       )
														image_processor.push_to_hub(
														    repo_path_or_name=Path(UpperCamelCase__				,       UpperCamelCase__				)				,       organization='''nielsr'''				,       commit_message='''Add image processor'''				,       use_temp_dir=UpperCamelCase__				,       )


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()

			parser.add_argument(
			    "--checkpoint_path",
			    default=None,
			    type=str,
			    help="Path 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", help="Whether to upload the model to the HuggingFace hub."
			)
			parser.add_argument(
			    "--model_name",
			    default="glpn-kitti",
			    type=str,
			    help="Name of the model in case you're pushing to the hub.",
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__				)

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






