



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_    :					int             ={
    """vit_b32""": {
        """embed_dim""": 512,
        """image_resolution""": 768,
        """context_length""": 16,
        """vocab_size""": 49408,
        """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""": 49408,
        """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""": 49408,
        """vision_layers""": 24,
        """vision_width""": 1024,
        """vision_patch_size""": 14,
        """transformer_width""": 768,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__=""       ,    UpperCAmelCase__="/"      ):



						"""simple docstring"""




						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__      ):



						"""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__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		pt_linear.weight
						a_					=		pt_linear.bias




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""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__      ):



						"""simple docstring"""




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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						a_					=		pt_model.text_projection.data.T

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




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						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__      ):



						"""simple docstring"""




						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      ):



						"""simple docstring"""




						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__":
	A_    :					Any             =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_    :					Dict             =parser.parse_args()

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

	# Load from checkpoint and convert params to float-32
	A_    :					Dict             =checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
	A_    :					int             =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_    :					Tuple             =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 os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class        lowercase_				( UpperCamelCase__			,unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		MPNetTokenizer
							snake_case_        =		MPNetTokenizerFast
							snake_case_        =		True
							snake_case_        =		True


							def 						lowercase__     (   self		):







													"""simple docstring"""


													super().setUp()

													a_					=		[
													    """[UNK]""",
													    """[CLS]""",
													    """[SEP]""",
													    """[PAD]""",
													    """[MASK]""",
													    """want""",
													    """##want""",
													    """##ed""",
													    """wa""",
													    """un""",
													    """runn""",
													    """##ing""",
													    """,""",
													    """low""",
													    """lowest""",
													]
													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		"""UNwant\u00E9d,running"""
													a_					=		"""unwanted, running"""
													return input_text, output_text


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.tokenizer_class(self.vocab_file		)

													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)






							@slow
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)

													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)

													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)

													assert encoded_sentence == [0] + text + [2]
													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]