
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,
)


SCREAMING_SNAKE_CASE   :		Optional[Any]					 =		{
    "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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase="" ,						__UpperCamelCase="/"     ):
				A_       	=  []

				for k, v in params.items():
								A_       	=  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     ):
				return jax.tree_util.tree_map(lambda __UpperCamelCase     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x ,						__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_	,					A_       	=  pt_attn_layer.in_proj_weight.chunk(3 ,						dim=0     )
				A_	,					A_	,					A_       	=  pt_attn_layer.in_proj_bias.chunk(3 ,						dim=0     )

				A_       	=  pt_attn_layer.out_proj.weight
				A_       	=  pt_attn_layer.out_proj.bias

				A_       	=  q_proj
				A_       	=  q_proj_bias

				A_       	=  k_proj
				A_       	=  k_proj_bias

				A_       	=  v_proj
				A_       	=  v_proj_bias

				A_       	=  out_proj_weights
				A_       	=  out_proj_bias



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				copy_linear(hf_mlp.fca ,						pt_mlp.c_fc     )
				copy_linear(hf_mlp.fca ,						pt_mlp.c_proj     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  pt_linear.weight
				A_       	=  pt_linear.bias



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# copy layer norms
				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     ):
				for hf_layer, pt_layer in zip(__UpperCamelCase ,						__UpperCamelCase     ):
								copy_layer(__UpperCamelCase ,						__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# copy  embeds
				A_       	=  pt_model.token_embedding.weight
				A_       	=  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     ):
				# copy projection
				A_       	=  pt_model.text_projection.data.T

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# copy projection
				A_       	=  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
				A_       	=  pt_model.visual.conva.weight.data
				A_       	=  pt_model.visual.class_embedding
				A_       	=  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     ):
				A_       	=  flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']     )

				A_       	=  torch.from_numpy(flax_class_token_params['''scale''']     )
				A_       	=  torch.from_numpy(flax_class_token_params['''bias''']     )
				A_       	=  nn.Parameter(__UpperCamelCase     )
				A_       	=  nn.Parameter(__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  hf_model.state_dict()
				A_       	=  {}

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

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

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

								A_       	=  nn.Parameter(torch.from_numpy(__UpperCamelCase     )     )

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

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

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

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

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

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

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

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

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



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  {}
				A_       	=  []

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

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  CLIP(**__UpperCamelCase     )
				torch_model.eval()
				A_       	=  torch_model.state_dict()

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

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

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

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

								A_       	=  v

				A_       	=  _convert_attn_layers(__UpperCamelCase     )
				new_torch_params.update(__UpperCamelCase     )
				A_       	=  {}

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

				return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=None     ):
				A_       	=  Repository(__UpperCamelCase ,						clone_from=F"google/{pytorch_dump_folder_path}"     )
				repo.git_pull()

				if config_path is not None:
								A_       	=  OwlViTConfig.from_pretrained(__UpperCamelCase     )
				else:
								A_       	=  OwlViTConfig()

				A_       	=  OwlViTModel(__UpperCamelCase     ).eval()
				A_       	=  OwlViTForObjectDetection(__UpperCamelCase     ).eval()

				copy_text_model_and_projection(__UpperCamelCase ,						__UpperCamelCase     )
				copy_vision_model_and_projection(__UpperCamelCase ,						__UpperCamelCase     )
				A_       	=  pt_backbone.logit_scale
				copy_flax_attn_params(__UpperCamelCase ,						__UpperCamelCase     )

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

				# Initialize processor
				A_       	=  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__":
						SCREAMING_SNAKE_CASE   :		int					 =		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."
						)
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =		parser.parse_args()

						# Initialize PyToch clip model
						SCREAMING_SNAKE_CASE   :		List[str]					 =		args.owlvit_version
						if model_name == "clip_b16":
												SCREAMING_SNAKE_CASE   :		int					 =		CONFIGS["vit_b16"]
						elif model_name == "clip_b32":
												SCREAMING_SNAKE_CASE   :		List[str]					 =		CONFIGS["vit_b32"]
						elif model_name == "clip_l14":
												SCREAMING_SNAKE_CASE   :		Any					 =		CONFIGS["vit_l14"]

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

						# Convert CLIP backbone
						SCREAMING_SNAKE_CASE   :		List[str]					 =		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 math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


SCREAMING_SNAKE_CASE   :		str					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		List[str]					 =       [
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]






class 					__lowercase		(					A       ):
					__magic_name__						:							Any     	=	RetriBertConfig
					__magic_name__						:							Optional[Any]     	=	None
					__magic_name__						:							str     	=	'''retribert'''


					def        lowerCAmelCase_    (	self       ,       a__						)		->							int:





									'''simple docstring'''
									if isinstance(a__       ,       nn.Linear						):
													module.weight.data.normal_(mean=0.0       ,       std=self.config.initializer_range						)
													if module.bias is not None:
																	module.bias.data.zero_()
									elif isinstance(a__       ,       nn.Embedding						):
													module.weight.data.normal_(mean=0.0       ,       std=self.config.initializer_range						)
													if module.padding_idx is not None:
																	module.weight.data[module.padding_idx].zero_()
									elif isinstance(a__       ,       nn.LayerNorm						):
													module.bias.data.zero_()
													module.weight.data.fill_(1.0						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       R"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"






@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''							,       A							,       )
class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							None:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  config.projection_dim

									A_       	=  BertModel(a__						)
									A_       	=  None if config.share_encoders else BertModel(a__						)
									A_       	=  nn.Dropout(config.hidden_dropout_prob						)
									A_       	=  nn.Linear(config.hidden_size       ,       config.projection_dim       ,       bias=a__						)
									A_       	=  nn.Linear(config.hidden_size       ,       config.projection_dim       ,       bias=a__						)

									A_       	=  nn.CrossEntropyLoss(reduction='''mean'''						)

									# Initialize weights and apply final processing
									self.post_init()


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__=-1       ,       )		->							int:





									'''simple docstring'''
									# reproduces BERT forward pass with checkpointing
									if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
													return sent_encoder(a__       ,       attention_mask=a__						)[1]
									else:
													# prepare implicit variables
													A_       	=  input_ids.device
													A_       	=  input_ids.size()
													A_       	=  torch.zeros(a__       ,       dtype=torch.long       ,       device=a__						)
													A_       	=  [None] * sent_encoder.config.num_hidden_layers
													A_       	=  sent_encoder.get_extended_attention_mask(
													    a__       ,       a__						)

													# define function for checkpointing
													def partial_encode(*a__						):
																	A_       	=  sent_encoder.encoder(
																	    inputs[0]       ,       attention_mask=inputs[1]       ,       head_mask=a__       ,       )
																	A_       	=  encoder_outputs[0]
																	A_       	=  sent_encoder.pooler(a__						)
																	return pooled_output

													# run embedding layer on everything at once
													A_       	=  sent_encoder.embeddings(
													    input_ids=a__       ,       position_ids=a__       ,       token_type_ids=a__       ,       inputs_embeds=a__						)
													# run encoding and pooling on one mini-batch at a time
													A_       	=  []
													for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
																	A_       	=  embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																	A_       	=  extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																	A_       	=  checkpoint.checkpoint(a__       ,       a__       ,       a__						)
																	pooled_output_list.append(a__						)
													return torch.cat(a__       ,       dim=0						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=None       ,       a__=-1       ,       )		->							int:





									'''simple docstring'''
									A_       	=  self.embed_sentences_checkpointed(
									    a__       ,       a__       ,       self.bert_query       ,       a__       ,       )
									return self.project_query(a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__=None       ,       a__=-1       ,       )		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  self.embed_sentences_checkpointed(
									    a__       ,       a__       ,       self.bert_query if self.bert_doc is None else self.bert_doc       ,       a__       ,       )
									return self.project_doc(a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__ = -1       ,       )		->							torch.FloatTensor:





									'''simple docstring'''
									A_       	=  input_ids_query.device
									A_       	=  self.embed_questions(a__       ,       a__       ,       a__						)
									A_       	=  self.embed_answers(a__       ,       a__       ,       a__						)
									A_       	=  torch.mm(a__       ,       a_reps.t()						)
									A_       	=  self.ce_loss(a__       ,       torch.arange(compare_scores.shape[1]						).to(a__						)						)
									A_       	=  self.ce_loss(compare_scores.t()       ,       torch.arange(compare_scores.shape[0]						).to(a__						)						)
									A_       	=  (loss_qa + loss_aq) / 2
									return loss