





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,
)


lowerCAmelCase_												=       {
    '''vit_b32''': {
        '''embed_dim''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 3_2,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_1_2,
        '''image_resolution''': 7_6_8,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 1_2,
        '''vision_width''': 7_6_8,
        '''vision_patch_size''': 1_6,
        '''transformer_width''': 5_1_2,
        '''transformer_heads''': 8,
        '''transformer_layers''': 1_2,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_6_8,
        '''image_resolution''': 8_4_0,
        '''context_length''': 1_6,
        '''vocab_size''': 4_9_4_0_8,
        '''vision_layers''': 2_4,
        '''vision_width''': 1_0_2_4,
        '''vision_patch_size''': 1_4,
        '''transformer_width''': 7_6_8,
        '''transformer_heads''': 1_2,
        '''transformer_layers''': 1_2,
    },
}






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=""				,       _UpperCamelCase="/"				)	->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:					str			    =						[]

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

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






def   lowerCamelCase_		(       _UpperCamelCase				)	->		int:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


				snake_case_    ,							snake_case_    ,							snake_case_		:					Optional[Any]			    =						pt_attn_layer.in_proj_weight.chunk(3				,       dim=0				)
				snake_case_    ,							snake_case_    ,							snake_case_		:					List[str]			    =						pt_attn_layer.in_proj_bias.chunk(3				,       dim=0				)

				snake_case_		:					Any			    =						pt_attn_layer.out_proj.weight
				snake_case_		:					Any			    =						pt_attn_layer.out_proj.bias

				snake_case_		:					Optional[int]			    =						q_proj
				snake_case_		:					List[str]			    =						q_proj_bias

				snake_case_		:					Optional[int]			    =						k_proj
				snake_case_		:					Union[str, Any]			    =						k_proj_bias

				snake_case_		:					Any			    =						v_proj
				snake_case_		:					Union[str, Any]			    =						v_proj_bias

				snake_case_		:					Any			    =						out_proj_weights
				snake_case_		:					int			    =						out_proj_bias






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Any:






				"""simple docstring"""


				snake_case_		:					Optional[int]			    =						pt_linear.weight
				snake_case_		:					Tuple			    =						pt_linear.bias






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Dict:






				"""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   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		int:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Dict:






				"""simple docstring"""


				snake_case_		:					Any			    =						pt_model.token_embedding.weight
				snake_case_		:					Dict			    =						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   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:					List[Any]			    =						pt_model.text_projection.data.T

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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:					List[str]			    =						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
				snake_case_		:					List[Any]			    =						pt_model.visual.conva.weight.data
				snake_case_		:					Union[str, Any]			    =						pt_model.visual.class_embedding
				snake_case_		:					List[str]			    =						pt_model.visual.positional_embedding.data

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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		List[Any]:






				"""simple docstring"""


				snake_case_		:					Optional[int]			    =						flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']				)

				snake_case_		:					int			    =						torch.from_numpy(flax_class_token_params['''scale''']				)
				snake_case_		:					List[Any]			    =						torch.from_numpy(flax_class_token_params['''bias''']				)
				snake_case_		:					int			    =						nn.Parameter(_UpperCamelCase				)
				snake_case_		:					Optional[Any]			    =						nn.Parameter(_UpperCamelCase				)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[int]:






				"""simple docstring"""


				snake_case_		:					List[str]			    =						hf_model.state_dict()
				snake_case_		:					Tuple			    =						{}

				# Rename class prediction head flax params to pytorch HF
				snake_case_		:					Union[str, Any]			    =						flatten_nested_dict(flax_params['''class_head''']				)

				for flax_key, v in flax_class_params.items():
								snake_case_		:					str			    =						flax_key.replace('''/'''				,       '''.'''				)
								snake_case_		:					List[str]			    =						torch_key.replace('''.kernel'''				,       '''.weight'''				)
								snake_case_		:					Tuple			    =						torch_key.replace('''Dense_0'''				,       '''dense0'''				)
								snake_case_		:					Tuple			    =						'''class_head.''' + torch_key

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

								snake_case_		:					int			    =						nn.Parameter(torch.from_numpy(_UpperCamelCase				)				)

				# Rename box prediction box flax params to pytorch HF
				snake_case_		:					List[Any]			    =						flatten_nested_dict(flax_params['''obj_box_head''']				)

				for flax_key, v in flax_box_params.items():
								snake_case_		:					str			    =						flax_key.replace('''/'''				,       '''.'''				)
								snake_case_		:					str			    =						torch_key.replace('''.kernel'''				,       '''.weight'''				)
								snake_case_		:					Dict			    =						torch_key.replace('''_'''				,       ''''''				).lower()
								snake_case_		:					Dict			    =						'''box_head.''' + torch_key

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

								snake_case_		:					Any			    =						nn.Parameter(torch.from_numpy(_UpperCamelCase				)				)

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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		List[str]:






				"""simple docstring"""


				for k, v in flax_attn_params.items():
								if k.startswith('''transformer'''				):
												snake_case_		:					Tuple			    =						k.replace('''transformer.resblocks'''				,       '''text_model.encoder.layers'''				)
								else:
												snake_case_		:					str			    =						k.replace('''visual.transformer.resblocks'''				,       '''vision_model.encoder.layers'''				)

								snake_case_		:					Dict			    =						torch_key.replace('''attn'''				,       '''self_attn'''				)
								snake_case_		:					Tuple			    =						torch_key.replace('''key'''				,       '''k_proj'''				)
								snake_case_		:					Tuple			    =						torch_key.replace('''value'''				,       '''v_proj'''				)
								snake_case_		:					Dict			    =						torch_key.replace('''query'''				,       '''q_proj'''				)
								snake_case_		:					List[Any]			    =						torch_key.replace('''out'''				,       '''out_proj'''				)

								if "bias" in torch_key and v.ndim == 2:
												snake_case_		:					int			    =						v.shape[0] * v.shape[1]
												snake_case_		:					str			    =						v.reshape(_UpperCamelCase				)

								if "weight" in torch_key and "out" in torch_key:
												snake_case_		:					Any			    =						(v.shape[0] * v.shape[1], v.shape[2])
												snake_case_		:					Union[str, Any]			    =						v.reshape(_UpperCamelCase				).T

								if "weight" in torch_key and "out" not in torch_key:
												snake_case_		:					Optional[Any]			    =						(v.shape[0], v.shape[1] * v.shape[2])
												snake_case_		:					str			    =						v.reshape(_UpperCamelCase				).T

								# Copy flax CLIP attn params to HF PyTorch params
								snake_case_		:					List[str]			    =						torch.from_numpy(_UpperCamelCase				)
								hf_backbone.state_dict()[torch_key].copy_(_UpperCamelCase				)






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Dict:






				"""simple docstring"""


				snake_case_		:					List[str]			    =						{}
				snake_case_		:					Optional[Any]			    =						[]

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

												processed_attn_layers.append(_UpperCamelCase				)
												snake_case_		:					List[Any]			    =						params[base + '''out.weight'''].shape[-1]
												snake_case_		:					str			    =						params[base + '''out.weight'''].reshape(_UpperCamelCase				,       _UpperCamelCase				).T
												snake_case_		:					str			    =						params[base + '''out.bias''']
								else:
												snake_case_		:					Dict			    =						v
				return new_params






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		List[Any]:






				"""simple docstring"""


				snake_case_		:					Union[str, Any]			    =						CLIP(**_UpperCamelCase				)
				torch_model.eval()
				snake_case_		:					Union[str, Any]			    =						torch_model.state_dict()

				snake_case_		:					Any			    =						flatten_nested_dict(flax_params['''backbone''']['''clip''']				)
				snake_case_		:					Tuple			    =						{}

				for flax_key, v in flax_clip_params.items():
								snake_case_		:					Dict			    =						flax_key.replace('''/'''				,       '''.'''				)
								snake_case_		:					List[Any]			    =						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'''				)
								):
												snake_case_		:					List[str]			    =						torch_key[5:]

								snake_case_		:					Optional[Any]			    =						torch_key.replace('''text_projection.kernel'''				,       '''text_projection'''				)
								snake_case_		:					List[str]			    =						torch_key.replace('''visual.proj.kernel'''				,       '''visual.proj'''				)
								snake_case_		:					List[Any]			    =						torch_key.replace('''.scale'''				,       '''.weight'''				)
								snake_case_		:					Union[str, Any]			    =						torch_key.replace('''.kernel'''				,       '''.weight'''				)

								if "conv" in torch_key or "downsample.0.weight" in torch_key:
												snake_case_		:					Optional[Any]			    =						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
												snake_case_		:					Dict			    =						v.T

								snake_case_		:					int			    =						v

				snake_case_		:					Union[str, Any]			    =						_convert_attn_layers(_UpperCamelCase				)
				new_torch_params.update(_UpperCamelCase				)
				snake_case_		:					Optional[int]			    =						{}

				# Copy flax CLIP backbone params to PyTorch params
				for name, param in new_torch_params.items():
								if name in torch_clip_params.keys():
												snake_case_		:					Union[str, Any]			    =						torch.from_numpy(new_torch_params[name]				)
												torch_clip_params[name].copy_(_UpperCamelCase				)
								else:
												snake_case_		:					int			    =						param

				return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase=None				)	->		int:






				"""simple docstring"""


				snake_case_		:					Union[str, Any]			    =						Repository(_UpperCamelCase				,       clone_from=f'''google/{pytorch_dump_folder_path}'''				)
				repo.git_pull()

				if config_path is not None:
								snake_case_		:					Dict			    =						OwlViTConfig.from_pretrained(_UpperCamelCase				)
				else:
								snake_case_		:					Tuple			    =						OwlViTConfig()

				snake_case_		:					Dict			    =						OwlViTModel(_UpperCamelCase				).eval()
				snake_case_		:					Optional[Any]			    =						OwlViTForObjectDetection(_UpperCamelCase				).eval()

				copy_text_model_and_projection(_UpperCamelCase				,       _UpperCamelCase				)
				copy_vision_model_and_projection(_UpperCamelCase				,       _UpperCamelCase				)
				snake_case_		:					List[Any]			    =						pt_backbone.logit_scale
				copy_flax_attn_params(_UpperCamelCase				,       _UpperCamelCase				)

				snake_case_		:					Tuple			    =						hf_backbone
				copy_class_merge_token(_UpperCamelCase				,       _UpperCamelCase				)
				copy_class_box_heads(_UpperCamelCase				,       _UpperCamelCase				)

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

				# Initialize image processor
				snake_case_		:					str			    =						OwlViTImageProcessor(
				    size=config.vision_config.image_size				,       crop_size=config.vision_config.image_size				)
				# Initialize tokenizer
				snake_case_		:					Optional[int]			    =						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				,       pad_token='''!'''				,       model_max_length=16				)

				# Initialize processor
				snake_case_		:					Union[str, Any]			    =						OwlViTProcessor(image_processor=_UpperCamelCase				,       tokenizer=_UpperCamelCase				)
				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__":
	lowerCAmelCase_												=       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.'''
	)
	lowerCAmelCase_												=       parser.parse_args()

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

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

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_												=       {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa import MBartaaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
