
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,
)


UpperCAmelCase     				=			{
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 4_9408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 32,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 4_9408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 16,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 768,
        '''image_resolution''': 840,
        '''context_length''': 16,
        '''vocab_size''': 4_9408,
        '''vision_layers''': 24,
        '''vision_width''': 1024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=""			,							__SCREAMING_SNAKE_CASE="/"							):
	lowercase					  =   []

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

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE							: x.astype(jnp.floataa							) if x.dtype == jnp.bfloataa else x			,							lowercase_							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase  , lowercase  , lowercase					  =   pt_attn_layer.in_proj_weight.chunk(3			,							dim=0							)
	lowercase  , lowercase  , lowercase					  =   pt_attn_layer.in_proj_bias.chunk(3			,							dim=0							)

	lowercase					  =   pt_attn_layer.out_proj.weight
	lowercase					  =   pt_attn_layer.out_proj.bias

	lowercase					  =   q_proj
	lowercase					  =   q_proj_bias

	lowercase					  =   k_proj
	lowercase					  =   k_proj_bias

	lowercase					  =   v_proj
	lowercase					  =   v_proj_bias

	lowercase					  =   out_proj_weights
	lowercase					  =   out_proj_bias






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	copy_linear(hf_mlp.fca			,							pt_mlp.c_fc							)
	copy_linear(hf_mlp.fca			,							pt_mlp.c_proj							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   pt_linear.weight
	lowercase					  =   pt_linear.bias






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   pt_model.token_embedding.weight
	lowercase					  =   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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   pt_model.text_projection.data.T

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   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
	lowercase					  =   pt_model.visual.conva.weight.data
	lowercase					  =   pt_model.visual.class_embedding
	lowercase					  =   pt_model.visual.positional_embedding.data

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   flatten_nested_dict(flax_params['backbone']['merged_class_token']							)

	lowercase					  =   torch.from_numpy(flax_class_token_params['scale']							)
	lowercase					  =   torch.from_numpy(flax_class_token_params['bias']							)
	lowercase					  =   nn.Parameter(lowercase_							)
	lowercase					  =   nn.Parameter(lowercase_							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   hf_model.state_dict()
	lowercase					  =   {}

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

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

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

		lowercase					  =   nn.Parameter(torch.from_numpy(lowercase_							)							)

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

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

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

		lowercase					  =   nn.Parameter(torch.from_numpy(lowercase_							)							)

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):

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

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

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

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

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

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   {}
	lowercase					  =   []

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

			processed_attn_layers.append(lowercase_							)
			lowercase					  =   params[base + 'out.weight'].shape[-1]
			lowercase					  =   params[base + 'out.weight'].reshape(lowercase_			,							lowercase_							).T
			lowercase					  =   params[base + 'out.bias']
		else:
			lowercase					  =   v
	return new_params






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   CLIP(**lowercase_							)
	torch_model.eval()
	lowercase					  =   torch_model.state_dict()

	lowercase					  =   flatten_nested_dict(flax_params['backbone']['clip']							)
	lowercase					  =   {}

	for flax_key, v in flax_clip_params.items():
		lowercase					  =   flax_key.replace('/'			,							'.'							)
		lowercase					  =   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'							)
		):
			lowercase					  =   torch_key[5:]

		lowercase					  =   torch_key.replace('text_projection.kernel'			,							'text_projection'							)
		lowercase					  =   torch_key.replace('visual.proj.kernel'			,							'visual.proj'							)
		lowercase					  =   torch_key.replace('.scale'			,							'.weight'							)
		lowercase					  =   torch_key.replace('.kernel'			,							'.weight'							)

		if "conv" in torch_key or "downsample.0.weight" in torch_key:
			lowercase					  =   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
			lowercase					  =   v.T

		lowercase					  =   v

	lowercase					  =   _convert_attn_layers(lowercase_							)
	new_torch_params.update(lowercase_							)
	lowercase					  =   {}

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

	return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=None							):
	lowercase					  =   Repository(lowercase_			,							clone_from=F'''google/{pytorch_dump_folder_path}'''							)
	repo.git_pull()

	if config_path is not None:
		lowercase					  =   OwlViTConfig.from_pretrained(lowercase_							)
	else:
		lowercase					  =   OwlViTConfig()

	lowercase					  =   OwlViTModel(lowercase_							).eval()
	lowercase					  =   OwlViTForObjectDetection(lowercase_							).eval()

	copy_text_model_and_projection(lowercase_			,							lowercase_							)
	copy_vision_model_and_projection(lowercase_			,							lowercase_							)
	lowercase					  =   pt_backbone.logit_scale
	copy_flax_attn_params(lowercase_			,							lowercase_							)

	lowercase					  =   hf_backbone
	copy_class_merge_token(lowercase_			,							lowercase_							)
	copy_class_box_heads(lowercase_			,							lowercase_							)

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

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

	# Initialize processor
	lowercase					  =   OwlViTProcessor(image_processor=lowercase_			,							tokenizer=lowercase_							)
	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__":
	UpperCAmelCase     				=			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.'''
	)
	UpperCAmelCase     				=			parser.parse_args()

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

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

	# Convert CLIP backbone
	UpperCAmelCase				,			UpperCAmelCase				,			UpperCAmelCase     				=			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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=0							):
	lowercase					  =   np.arange(start=3			,							stop=__SCREAMING_SNAKE_CASE			,							step=4							).tolist() if k > 0 else [10]
	lowercase					  =   _quantize(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	# count number of elements per group
	lowercase					  =   np.unique(__SCREAMING_SNAKE_CASE			,							return_counts=__SCREAMING_SNAKE_CASE							)[1]
	lowercase					  =   [0] + bins + [np.inf]
	logger.info('Using {} as bins for aspect lengths quantization'.format(__SCREAMING_SNAKE_CASE							)							)
	logger.info('Count of instances per bin: {}'.format(__SCREAMING_SNAKE_CASE							)							)
	return groups







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(snake_case	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			if len(buffer_per_group[group_id]	) == self.batch_size:
				yield buffer_per_group[group_id]  # TODO
				num_batches += 1
				del buffer_per_group[group_id]
			assert len(buffer_per_group[group_id]	) < self.batch_size

		# now we have run out of elements that satisfy
		# the group criteria, let's return the remaining
		# elements so that the size of the sampler is
		# deterministic
		lowercase					  =   len(self	)
		lowercase					  =   expected_num_batches - num_batches
		if num_remaining > 0:
			# for the remaining batches, group the batches by similar lengths
			lowercase					  =   []
			for group_id, idxs in sorted(buffer_per_group.items()		,   key=lambda snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 0:
				yield batch_idx
				num_remaining -= 1
		assert num_remaining == 0





	def __len__(	self	):
		return (len(self.sampler	) + self.batch_size - 1) // self.batch_size

