



import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


A__			   = {
    """vit_b32""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 32,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_b16""": {
        """embed_dim""": 5_12,
        """image_resolution""": 7_68,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 12,
        """vision_width""": 7_68,
        """vision_patch_size""": 16,
        """transformer_width""": 5_12,
        """transformer_heads""": 8,
        """transformer_layers""": 12,
    },
    """vit_l14""": {
        """embed_dim""": 7_68,
        """image_resolution""": 8_40,
        """context_length""": 16,
        """vocab_size""": 4_94_08,
        """vision_layers""": 24,
        """vision_width""": 10_24,
        """vision_patch_size""": 14,
        """transformer_width""": 7_68,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}


def 	_UpperCAmelCase       (   snake_case			,		snake_case=""			,		snake_case="/"    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					[]

					for k, v in params.items():
										_lowerCAmelCase      			=					parent_key + sep + k if parent_key else k

										if isinstance(snake_case			,		collections.MutableMapping    ):
															items.extend(flatten_nested_dict(snake_case			,		snake_case			,		sep=snake_case    ).items()    )
										else:
															items.append((new_key, v)    )
					return dict(snake_case    )


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					return jax.tree_util.tree_map(lambda snake_case    : x.astype(jnp.floataa    ) if x.dtype == jnp.bfloataa else x			,		snake_case    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					pt_attn_layer.in_proj_weight.chunk(3			,		dim=0    )
					_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					pt_attn_layer.in_proj_bias.chunk(3			,		dim=0    )

					_lowerCAmelCase      			=					pt_attn_layer.out_proj.weight
					_lowerCAmelCase      			=					pt_attn_layer.out_proj.bias

					_lowerCAmelCase      			=					q_proj
					_lowerCAmelCase      			=					q_proj_bias

					_lowerCAmelCase      			=					k_proj
					_lowerCAmelCase      			=					k_proj_bias

					_lowerCAmelCase      			=					v_proj
					_lowerCAmelCase      			=					v_proj_bias

					_lowerCAmelCase      			=					out_proj_weights
					_lowerCAmelCase      			=					out_proj_bias


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					copy_linear(hf_mlp.fca			,		pt_mlp.c_fc    )
					copy_linear(hf_mlp.fca			,		pt_mlp.c_proj    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					pt_linear.weight
					_lowerCAmelCase      			=					pt_linear.bias


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a    )
					copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a    )

					# copy MLP
					copy_mlp(hf_layer.mlp			,		pt_layer.mlp    )

					# copy attn
					copy_attn_layer(hf_layer.self_attn			,		pt_layer.attn    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					for hf_layer, pt_layer in zip(snake_case			,		snake_case    ):
										copy_layer(snake_case			,		snake_case    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					pt_model.token_embedding.weight
					_lowerCAmelCase      			=					pt_model.positional_embedding

					# copy layer norm
					copy_linear(hf_encoder.final_layer_norm			,		pt_model.ln_final    )

					# copy hidden layers
					copy_layers(hf_encoder.encoder.layers			,		pt_model.transformer.resblocks    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					pt_model.text_projection.data.T

					# copy text encoder
					copy_encoder(hf_model.text_model			,		snake_case    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					pt_model.visual.proj.data.T

					# copy layer norms
					copy_linear(hf_model.vision_model.pre_layernorm			,		pt_model.visual.ln_pre    )
					copy_linear(hf_model.vision_model.post_layernorm			,		pt_model.visual.ln_post    )

					# copy embeds
					_lowerCAmelCase      			=					pt_model.visual.conva.weight.data
					_lowerCAmelCase      			=					pt_model.visual.class_embedding
					_lowerCAmelCase      			=					pt_model.visual.positional_embedding.data

					# copy encoder
					copy_layers(hf_model.vision_model.encoder.layers			,		pt_model.visual.transformer.resblocks    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]    )

					_lowerCAmelCase      			=					torch.from_numpy(flax_class_token_params["""scale"""]    )
					_lowerCAmelCase      			=					torch.from_numpy(flax_class_token_params["""bias"""]    )
					_lowerCAmelCase      			=					nn.Parameter(snake_case    )
					_lowerCAmelCase      			=					nn.Parameter(snake_case    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					hf_model.state_dict()
					_lowerCAmelCase      			=					{}

					# Rename class prediction head flax params to pytorch HF
					_lowerCAmelCase      			=					flatten_nested_dict(flax_params["""class_head"""]    )

					for flax_key, v in flax_class_params.items():
										_lowerCAmelCase      			=					flax_key.replace("""/"""			,		"""."""    )
										_lowerCAmelCase      			=					torch_key.replace(""".kernel"""			,		""".weight"""    )
										_lowerCAmelCase      			=					torch_key.replace("""Dense_0"""			,		"""dense0"""    )
										_lowerCAmelCase      			=					"""class_head.""" + torch_key

										if "weight" in torch_key and v.ndim == 2:
															_lowerCAmelCase      			=					v.T

										_lowerCAmelCase      			=					nn.Parameter(torch.from_numpy(snake_case    )    )

					# Rename box prediction box flax params to pytorch HF
					_lowerCAmelCase      			=					flatten_nested_dict(flax_params["""obj_box_head"""]    )

					for flax_key, v in flax_box_params.items():
										_lowerCAmelCase      			=					flax_key.replace("""/"""			,		"""."""    )
										_lowerCAmelCase      			=					torch_key.replace(""".kernel"""			,		""".weight"""    )
										_lowerCAmelCase      			=					torch_key.replace("""_"""			,		""""""    ).lower()
										_lowerCAmelCase      			=					"""box_head.""" + torch_key

										if "weight" in torch_key and v.ndim == 2:
															_lowerCAmelCase      			=					v.T

										_lowerCAmelCase      			=					nn.Parameter(torch.from_numpy(snake_case    )    )

					# Copy flax params to PyTorch params
					for name, param in new_params.items():
										if name in pt_params.keys():
															pt_params[name].copy_(snake_case    )


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					for k, v in flax_attn_params.items():
										if k.startswith("""transformer"""    ):
															_lowerCAmelCase      			=					k.replace("""transformer.resblocks"""			,		"""text_model.encoder.layers"""    )
										else:
															_lowerCAmelCase      			=					k.replace("""visual.transformer.resblocks"""			,		"""vision_model.encoder.layers"""    )

										_lowerCAmelCase      			=					torch_key.replace("""attn"""			,		"""self_attn"""    )
										_lowerCAmelCase      			=					torch_key.replace("""key"""			,		"""k_proj"""    )
										_lowerCAmelCase      			=					torch_key.replace("""value"""			,		"""v_proj"""    )
										_lowerCAmelCase      			=					torch_key.replace("""query"""			,		"""q_proj"""    )
										_lowerCAmelCase      			=					torch_key.replace("""out"""			,		"""out_proj"""    )

										if "bias" in torch_key and v.ndim == 2:
															_lowerCAmelCase      			=					v.shape[0] * v.shape[1]
															_lowerCAmelCase      			=					v.reshape(snake_case    )

										if "weight" in torch_key and "out" in torch_key:
															_lowerCAmelCase      			=					(v.shape[0] * v.shape[1], v.shape[2])
															_lowerCAmelCase      			=					v.reshape(snake_case    ).T

										if "weight" in torch_key and "out" not in torch_key:
															_lowerCAmelCase      			=					(v.shape[0], v.shape[1] * v.shape[2])
															_lowerCAmelCase      			=					v.reshape(snake_case    ).T

										# Copy flax CLIP attn params to HF PyTorch params
										_lowerCAmelCase      			=					torch.from_numpy(snake_case    )
										hf_backbone.state_dict()[torch_key].copy_(snake_case    )


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					{}
					_lowerCAmelCase      			=					[]

					for k, v in params.items():
										if "attn." in k:
															_lowerCAmelCase      			=					k[: k.rindex("""attn."""    ) + 5]
															if base in processed_attn_layers:
																				continue

															processed_attn_layers.append(snake_case    )
															_lowerCAmelCase      			=					params[base + """out.weight"""].shape[-1]
															_lowerCAmelCase      			=					params[base + """out.weight"""].reshape(snake_case			,		snake_case    ).T
															_lowerCAmelCase      			=					params[base + """out.bias"""]
										else:
															_lowerCAmelCase      			=					v
					return new_params


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					CLIP(**snake_case    )
					torch_model.eval()
					_lowerCAmelCase      			=					torch_model.state_dict()

					_lowerCAmelCase      			=					flatten_nested_dict(flax_params["""backbone"""]["""clip"""]    )
					_lowerCAmelCase      			=					{}

					for flax_key, v in flax_clip_params.items():
										_lowerCAmelCase      			=					flax_key.replace("""/"""			,		"""."""    )
										_lowerCAmelCase      			=					torch_key.replace("""text.token_embedding.embedding"""			,		"""token_embedding.kernel"""    )

										if (
										    torch_key.startswith("""text.transformer"""    )
										    or torch_key.startswith("""text.text_projection"""    )
										    or torch_key.startswith("""text.ln_final"""    )
										    or torch_key.startswith("""text.positional_embedding"""    )
										):
															_lowerCAmelCase      			=					torch_key[5:]

										_lowerCAmelCase      			=					torch_key.replace("""text_projection.kernel"""			,		"""text_projection"""    )
										_lowerCAmelCase      			=					torch_key.replace("""visual.proj.kernel"""			,		"""visual.proj"""    )
										_lowerCAmelCase      			=					torch_key.replace(""".scale"""			,		""".weight"""    )
										_lowerCAmelCase      			=					torch_key.replace(""".kernel"""			,		""".weight"""    )

										if "conv" in torch_key or "downsample.0.weight" in torch_key:
															_lowerCAmelCase      			=					v.transpose(3			,		2			,		0			,		1    )

										elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
															# Fully connected layers are transposed, embeddings are not
															_lowerCAmelCase      			=					v.T

										_lowerCAmelCase      			=					v

					_lowerCAmelCase      			=					_convert_attn_layers(snake_case    )
					new_torch_params.update(snake_case    )
					_lowerCAmelCase      			=					{}

					# Copy flax CLIP backbone params to PyTorch params
					for name, param in new_torch_params.items():
										if name in torch_clip_params.keys():
															_lowerCAmelCase      			=					torch.from_numpy(new_torch_params[name]    )
															torch_clip_params[name].copy_(snake_case    )
										else:
															_lowerCAmelCase      			=					param

					return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case=None    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					Repository(snake_case			,		clone_from=F'google/{pytorch_dump_folder_path}'    )
					repo.git_pull()

					if config_path is not None:
										_lowerCAmelCase      			=					OwlViTConfig.from_pretrained(snake_case    )
					else:
										_lowerCAmelCase      			=					OwlViTConfig()

					_lowerCAmelCase      			=					OwlViTModel(snake_case    ).eval()
					_lowerCAmelCase      			=					OwlViTForObjectDetection(snake_case    ).eval()

					copy_text_model_and_projection(snake_case			,		snake_case    )
					copy_vision_model_and_projection(snake_case			,		snake_case    )
					_lowerCAmelCase      			=					pt_backbone.logit_scale
					copy_flax_attn_params(snake_case			,		snake_case    )

					_lowerCAmelCase      			=					hf_backbone
					copy_class_merge_token(snake_case			,		snake_case    )
					copy_class_box_heads(snake_case			,		snake_case    )

					# Save HF model
					hf_model.save_pretrained(repo.local_dir    )

					# Initialize image processor
					_lowerCAmelCase      			=					OwlViTImageProcessor(
					    size=config.vision_config.image_size			,		crop_size=config.vision_config.image_size    )
					# Initialize tokenizer
					_lowerCAmelCase      			=					CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			,		pad_token="""!"""			,		model_max_length=16    )

					# Initialize processor
					_lowerCAmelCase      			=					OwlViTProcessor(image_processor=snake_case			,		tokenizer=snake_case    )
					image_processor.save_pretrained(repo.local_dir    )
					processor.save_pretrained(repo.local_dir    )

					repo.git_add()
					repo.git_commit("""Upload model and processor"""    )
					repo.git_push()


if __name__ == "__main__":
	A__			   = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--owlvit_version""",
	    default=None,
	    type=str,
	    required=True,
	    help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
	)
	parser.add_argument(
	    """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
	)
	parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
	)
	A__			   = parser.parse_args()

	# Initialize PyToch clip model
	A__			   = args.owlvit_version
	if model_name == "clip_b16":
		A__			   = CONFIGS["""vit_b16"""]
	elif model_name == "clip_b32":
		A__			   = CONFIGS["""vit_b32"""]
	elif model_name == "clip_l14":
		A__			   = CONFIGS["""vit_l14"""]

	# Load from checkpoint and convert params to float-32
	A__			   = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
	A__			   = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
	del variables

	# Convert CLIP backbone
	A__   ,    A__   ,    A__			   = convert_clip_backbone(flax_params, torch_config)

	convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)








import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A__			   = 1_80


def 	_UpperCAmelCase       (   snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					k_size // 2
					_lowerCAmelCase			,			_lowerCAmelCase      			=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					_lowerCAmelCase      			=					(
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(snake_case    ) + np.square(snake_case    )) / (2 * np.square(snake_case    ))    )
					)
					return g


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					np.zeros(snake_case    )

					for row in range(1			,		image_shape[0] - 1    ):
										for col in range(1			,		image_shape[1] - 1    ):
															_lowerCAmelCase      			=					gradient_direction[row, col]

															if (
															    0 <= direction < PI / 8
															    or 15 * PI / 8 <= direction <= 2 * PI
															    or 7 * PI / 8 <= direction <= 9 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row, col + 1]
																				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    PI / 8 <= direction < 3 * PI / 8
															    or 9 * PI / 8 <= direction < 11 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col + 1]
																				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    3 * PI / 8 <= direction < 5 * PI / 8
															    or 11 * PI / 8 <= direction < 13 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col]
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col]
																				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

															elif (
															    5 * PI / 8 <= direction < 7 * PI / 8
															    or 13 * PI / 8 <= direction < 15 * PI / 8
															):
																				_lowerCAmelCase      			=					sobel_grad[row - 1, col - 1]
																				_lowerCAmelCase      			=					sobel_grad[row + 1, col + 1]
																				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																									_lowerCAmelCase      			=					sobel_grad[row, col]

					return destination


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					for row in range(1			,		image_shape[0] - 1    ):
										for col in range(1			,		image_shape[1] - 1    ):
															if destination[row, col] >= threshold_high:
																				_lowerCAmelCase      			=					strong
															elif destination[row, col] <= threshold_low:
																				_lowerCAmelCase      			=					0
															else:
																				_lowerCAmelCase      			=					weak


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					for row in range(1			,		image_shape[0]    ):
										for col in range(1			,		image_shape[1]    ):
															if destination[row, col] == weak:
																				if 2_55 in (
																				    destination[row, col + 1],
																				    destination[row, col - 1],
																				    destination[row - 1, col],
																				    destination[row + 1, col],
																				    destination[row - 1, col - 1],
																				    destination[row + 1, col - 1],
																				    destination[row - 1, col + 1],
																				    destination[row + 1, col + 1],
																				):
																									_lowerCAmelCase      			=					strong
																				else:
																									_lowerCAmelCase      			=					0


def 	_UpperCAmelCase       (   snake_case			,		snake_case=15			,		snake_case=30			,		snake_case=1_28			,		snake_case=2_55    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					img_convolve(snake_case			,		gen_gaussian_kernel(9			,		sigma=1.4    )    )
					# get the gradient and degree by sobel_filter
					_lowerCAmelCase			,			_lowerCAmelCase      			=					sobel_filter(snake_case    )
					_lowerCAmelCase      			=					PI + np.radadeg(snake_case    )

					_lowerCAmelCase      			=					suppress_non_maximum(image.shape			,		snake_case			,		snake_case    )

					detect_high_low_threshold(
					    image.shape			,		snake_case			,		snake_case			,		snake_case			,		snake_case			,		snake_case    )

					track_edge(image.shape			,		snake_case			,		snake_case			,		snake_case    )

					return destination


if __name__ == "__main__":
	# read original image in gray mode
	A__			   = cva.imread(R"""../image_data/lena.jpg""", 0)
	# canny edge detection
	A__			   = canny(lena)
	cva.imshow("""canny""", canny_destination)
	cva.waitKey(0)



