
'''simple docstring'''
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''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49_408,
        '''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''': 49_408,
        '''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''': 49_408,
        '''vision_layers''': 24,
        '''vision_width''': 1_024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=""							,      _SCREAMING_SNAKE_CASE="/"   ):
							_snake_case     =						[]

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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case,							_snake_case,							_snake_case     =						pt_attn_layer.in_proj_weight.chunk(3							,      dim=0   )
							_snake_case,							_snake_case,							_snake_case     =						pt_attn_layer.in_proj_bias.chunk(3							,      dim=0   )

							_snake_case     =						pt_attn_layer.out_proj.weight
							_snake_case     =						pt_attn_layer.out_proj.bias

							_snake_case     =						q_proj
							_snake_case     =						q_proj_bias

							_snake_case     =						k_proj
							_snake_case     =						k_proj_bias

							_snake_case     =						v_proj
							_snake_case     =						v_proj_bias

							_snake_case     =						out_proj_weights
							_snake_case     =						out_proj_bias






def       __SCREAMING_SNAKE_CASE   (						_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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						pt_linear.weight
							_snake_case     =						pt_linear.bias






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							# 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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							for hf_layer, pt_layer in zip(snake_case__							,      snake_case__   ):
														copy_layer(snake_case__							,      snake_case__   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							# copy  embeds
							_snake_case     =						pt_model.token_embedding.weight
							_snake_case     =						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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							# copy projection
							_snake_case     =						pt_model.text_projection.data.T

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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							# copy projection
							_snake_case     =						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     =						pt_model.visual.conva.weight.data
							_snake_case     =						pt_model.visual.class_embedding
							_snake_case     =						pt_model.visual.positional_embedding.data

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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]   )

							_snake_case     =						torch.from_numpy(flax_class_token_params["""scale"""]   )
							_snake_case     =						torch.from_numpy(flax_class_token_params["""bias"""]   )
							_snake_case     =						nn.Parameter(snake_case__   )
							_snake_case     =						nn.Parameter(snake_case__   )






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						hf_model.state_dict()
							_snake_case     =						{}

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

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

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

														_snake_case     =						nn.Parameter(torch.from_numpy(snake_case__   )   )

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

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

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

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

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

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

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

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

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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						{}
							_snake_case     =						[]

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

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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						CLIP(**snake_case__   )
							torch_model.eval()
							_snake_case     =						torch_model.state_dict()

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

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

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

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

														_snake_case     =						v

							_snake_case     =						_convert_attn_layers(snake_case__   )
							new_torch_params.update(snake_case__   )
							_snake_case     =						{}

							# 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     =						torch.from_numpy(new_torch_params[name]   )
																					torch_clip_params[name].copy_(snake_case__   )
														else:
																					_snake_case     =						param

							return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=None   ):
							_snake_case     =						Repository(snake_case__							,      clone_from=f"""google/{pytorch_dump_folder_path}"""   )
							repo.git_pull()

							if config_path is not None:
														_snake_case     =						OwlViTConfig.from_pretrained(snake_case__   )
							else:
														_snake_case     =						OwlViTConfig()

							_snake_case     =						OwlViTModel(snake_case__   ).eval()
							_snake_case     =						OwlViTForObjectDetection(snake_case__   ).eval()

							copy_text_model_and_projection(snake_case__							,      snake_case__   )
							copy_vision_model_and_projection(snake_case__							,      snake_case__   )
							_snake_case     =						pt_backbone.logit_scale
							copy_flax_attn_params(snake_case__							,      snake_case__   )

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

							# Initialize processor
							_snake_case     =						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__":
				__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  							= convert_clip_backbone(flax_params, torch_config)

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

'''simple docstring'''
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
				from .tokenization_deberta_va import DebertaVaTokenizer
else:
				__lowerCAmelCase  							= None

__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__lowerCAmelCase  							= {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__lowerCAmelCase  							= {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		DebertaVaTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase=False						,						UpperCAmelCase=False						,						UpperCAmelCase="[CLS]"						,						UpperCAmelCase="[SEP]"						,						UpperCAmelCase="[UNK]"						,						UpperCAmelCase="[SEP]"						,						UpperCAmelCase="[PAD]"						,						UpperCAmelCase="[CLS]"						,						UpperCAmelCase="[MASK]"						,						**UpperCAmelCase						,						)  ->     None:
														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						do_lower_case=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						mask_token=UpperCAmelCase						,						split_by_punct=UpperCAmelCase						,						**UpperCAmelCase						,						)

														_snake_case     =						do_lower_case
														_snake_case     =						split_by_punct
														_snake_case     =						vocab_file
														_snake_case     =						False if not self.vocab_file else True

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=None						)  ->     List[Any]:

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_snake_case     =						[self.cls_token_id]
														_snake_case     =						[self.sep_token_id]
														return cls + token_ids_a + sep + token_ids_a + sep

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=None						,						UpperCAmelCase=False						)  ->     Union[str, Any]:

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCAmelCase						,						token_ids_a=UpperCAmelCase						,						already_has_special_tokens=UpperCAmelCase						)

														if token_ids_a is not None:
																					return [1] + ([0] * len(UpperCAmelCase						)) + [1] + ([0] * len(UpperCAmelCase						)) + [1]
														return [1] + ([0] * len(UpperCAmelCase						)) + [1]

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=None						)  ->     str:
														_snake_case     =						[self.sep_token_id]
														_snake_case     =						[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""						)

														if not os.path.isdir(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCAmelCase						):
																					copyfile(self.vocab_file						,						UpperCAmelCase						)

														return (out_vocab_file,)