


"""simple docstring"""






import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class 			SCREAMING_SNAKE_CASE_  (   lowerCamelCase_	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Dict				  =  ElectraTokenizer
							__magic_name__					:     Any				  =  ElectraTokenizerFast
							__magic_name__					:     Tuple				  =  True
							__magic_name__					:     List[Any]				  =  True
							__magic_name__					:     int				  =  filter_non_english




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								super().setUp()

								snake_case__  :						List[Any]		=      [
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								snake_case__  :						Optional[Any]		=      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        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      '''UNwant\u00E9d,running'''
								snake_case__  :						List[str]		=      '''unwanted, running'''
								return input_text, output_text




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Any		=      self.tokenizer_class(self.vocab_file)

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




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								if not self.test_rust_tokenizer:
									return

								snake_case__  :						int		=      self.get_tokenizer()
								snake_case__  :						Any		=      self.get_rust_tokenizer()

								snake_case__  :						str		=      '''UNwant\u00E9d,running'''

								snake_case__  :						Dict		=      tokenizer.tokenize(lowerCamelCase__)
								snake_case__  :						str		=      rust_tokenizer.tokenize(lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      tokenizer.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
								snake_case__  :						Optional[int]		=      rust_tokenizer.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

								snake_case__  :						List[str]		=      self.get_rust_tokenizer()
								snake_case__  :						int		=      tokenizer.encode(lowerCamelCase__)
								snake_case__  :						Dict		=      rust_tokenizer.encode(lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

								# With lower casing
								snake_case__  :						Union[str, Any]		=      self.get_tokenizer(do_lower_case=lowerCamelCase__)
								snake_case__  :						Optional[int]		=      self.get_rust_tokenizer(do_lower_case=lowerCamelCase__)

								snake_case__  :						Optional[int]		=      '''UNwant\u00E9d,running'''

								snake_case__  :						str		=      tokenizer.tokenize(lowerCamelCase__)
								snake_case__  :						Tuple		=      rust_tokenizer.tokenize(lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      tokenizer.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
								snake_case__  :						int		=      rust_tokenizer.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

								snake_case__  :						Any		=      self.get_rust_tokenizer()
								snake_case__  :						List[str]		=      tokenizer.encode(lowerCamelCase__)
								snake_case__  :						Any		=      rust_tokenizer.encode(lowerCamelCase__)
								self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      BasicTokenizer()

								self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz")	,					["ah", "\u535A", "\u63A8", "zz"])




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      BasicTokenizer(do_lower_case=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  ")	,					["hello", "!", "how", "are", "you", "?"])
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo")	,					["hello"])




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						int		=      BasicTokenizer(do_lower_case=lowerCamelCase__	,					strip_accents=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ")	,					["hällo", "!", "how", "are", "you", "?"])
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo")	,					["h\u00E9llo"])




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      BasicTokenizer(do_lower_case=lowerCamelCase__	,					strip_accents=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ")	,					["hallo", "!", "how", "are", "you", "?"])
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo")	,					["hello"])




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      BasicTokenizer(do_lower_case=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ")	,					["hallo", "!", "how", "are", "you", "?"])
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo")	,					["hello"])




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      BasicTokenizer(do_lower_case=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  ")	,					["HeLLo", "!", "how", "Are", "yoU", "?"])




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						Any		=      BasicTokenizer(do_lower_case=lowerCamelCase__	,					strip_accents=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ")	,					["HäLLo", "!", "how", "Are", "yoU", "?"])




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						int		=      BasicTokenizer(do_lower_case=lowerCamelCase__	,					strip_accents=lowerCamelCase__)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  ")	,					["HaLLo", "!", "how", "Are", "yoU", "?"])




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      BasicTokenizer(do_lower_case=lowerCamelCase__	,					never_split=["[UNK]"])

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]")	,					["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

								snake_case__  :						Any		=      {}
								for i, token in enumerate(lowerCamelCase__):
									snake_case__  :						List[str]		=      i
								snake_case__  :						Optional[int]		=      WordpieceTokenizer(vocab=lowerCamelCase__	,					unk_token="[UNK]")

								self.assertListEqual(tokenizer.tokenize("")	,					[])

								self.assertListEqual(tokenizer.tokenize("unwanted running")	,					["un", "##want", "##ed", "runn", "##ing"])

								self.assertListEqual(tokenizer.tokenize("unwantedX running")	,					["[UNK]", "runn", "##ing"])




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								self.assertTrue(_is_whitespace(" "))
								self.assertTrue(_is_whitespace("\t"))
								self.assertTrue(_is_whitespace("\r"))
								self.assertTrue(_is_whitespace("\n"))
								self.assertTrue(_is_whitespace("\u00A0"))

								self.assertFalse(_is_whitespace("A"))
								self.assertFalse(_is_whitespace("-"))




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								self.assertTrue(_is_control("\u0005"))

								self.assertFalse(_is_control("A"))
								self.assertFalse(_is_control(" "))
								self.assertFalse(_is_control("\t"))
								self.assertFalse(_is_control("\r"))




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								self.assertTrue(_is_punctuation("-"))
								self.assertTrue(_is_punctuation("$"))
								self.assertTrue(_is_punctuation("`"))
								self.assertTrue(_is_punctuation("."))

								self.assertFalse(_is_punctuation("A"))
								self.assertFalse(_is_punctuation(" "))




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						int		=      self.get_tokenizer()
								snake_case__  :						List[str]		=      self.get_rust_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(lowerCamelCase__) for t in ["Test", "\xad", "test"]]	,					[["[UNK]"], [], ["[UNK]"]])

								self.assertListEqual(
								    [rust_tokenizer.tokenize(lowerCamelCase__) for t in ["Test", "\xad", "test"]]	,					[["[UNK]"], [], ["[UNK]"]])




							@slow
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.tokenizer_class.from_pretrained("google/electra-base-discriminator")

								snake_case__  :						Tuple		=      tokenizer.encode("sequence builders"	,					add_special_tokens=lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      tokenizer.encode("multi-sequence build"	,					add_special_tokens=lowerCamelCase__)

								snake_case__  :						List[str]		=      tokenizer.build_inputs_with_special_tokens(lowerCamelCase__)
								snake_case__  :						Optional[int]		=      tokenizer.build_inputs_with_special_tokens(lowerCamelCase__	,					lowerCamelCase__)

								assert encoded_sentence == [101] + text + [102]
								assert encoded_pair == [101] + text + [102] + text_a + [102]




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										snake_case__  :						str		=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)

										snake_case__  :						List[str]		=      f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										snake_case__  :						str		=      tokenizer_r.encode_plus(
										    lowerCamelCase__	,					return_attention_mask=lowerCamelCase__	,					return_token_type_ids=lowerCamelCase__	,					return_offsets_mapping=lowerCamelCase__	,					add_special_tokens=lowerCamelCase__	,					)

										snake_case__  :						List[str]		=      tokenizer_r.do_lower_case if hasattr(lowerCamelCase__	,					"do_lower_case") else False
										snake_case__  :						str		=      (
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), '''A'''),
										        ((1, 2), ''','''),
										        ((3, 5), '''na'''),
										        ((5, 6), '''##ï'''),
										        ((6, 8), '''##ve'''),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), '''Allen'''),
										        ((21, 23), '''##NL'''),
										        ((23, 24), '''##P'''),
										        ((25, 33), '''sentence'''),
										        ((33, 34), '''.'''),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), '''a'''),
										        ((1, 2), ''','''),
										        ((3, 8), '''naive'''),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), '''allen'''),
										        ((21, 23), '''##nl'''),
										        ((23, 24), '''##p'''),
										        ((25, 33), '''sentence'''),
										        ((33, 34), '''.'''),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]	,					tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
										self.assertEqual([e[0] for e in expected_results]	,					tokens["offset_mapping"])







							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Dict		=      ['''的''', '''人''', '''有''']
								snake_case__  :						str		=      ''''''.join(lowerCamelCase__)
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										snake_case__  :						Optional[Any]		=      True
										snake_case__  :						List[Any]		=      self.tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)
										snake_case__  :						str		=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)

										snake_case__  :						Dict		=      tokenizer_p.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
										snake_case__  :						int		=      tokenizer_r.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)

										snake_case__  :						str		=      tokenizer_r.convert_ids_to_tokens(lowerCamelCase__)
										snake_case__  :						Union[str, Any]		=      tokenizer_p.convert_ids_to_tokens(lowerCamelCase__)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)
										self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)

										snake_case__  :						Optional[Any]		=      False
										snake_case__  :						Optional[Any]		=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)
										snake_case__  :						Optional[int]		=      self.tokenizer_class.from_pretrained(lowerCamelCase__	,					**lowerCamelCase__)

										snake_case__  :						int		=      tokenizer_r.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)
										snake_case__  :						Union[str, Any]		=      tokenizer_p.encode(lowerCamelCase__	,					add_special_tokens=lowerCamelCase__)

										snake_case__  :						str		=      tokenizer_r.convert_ids_to_tokens(lowerCamelCase__)
										snake_case__  :						Union[str, Any]		=      tokenizer_p.convert_ids_to_tokens(lowerCamelCase__)

										# it is expected that only the first Chinese character is not preceded by "##".
										snake_case__  :						Any		=      [
										    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCamelCase__)
										]
										self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)
										self.assertListEqual(lowerCamelCase__	,					lowerCamelCase__)











'''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,
)


lowercase						:   Any      						=							{
    '''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 		a_(			lowerCamelCase ,					lowerCamelCase="" ,					lowerCamelCase="/"   )					->str:






       '''simple docstring'''




       lowercase	:		List[Any]    =   []

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

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




def 		a_(			lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase,  lowercase,  lowercase	:		Optional[Any]    =   pt_attn_layer.in_proj_weight.chunk(3 ,					dim=0   )
       lowercase,  lowercase,  lowercase	:		Optional[int]    =   pt_attn_layer.in_proj_bias.chunk(3 ,					dim=0   )

       lowercase	:		Union[str, Any]    =   pt_attn_layer.out_proj.weight
       lowercase	:		Tuple    =   pt_attn_layer.out_proj.bias

       lowercase	:		int    =   q_proj
       lowercase	:		Optional[Any]    =   q_proj_bias

       lowercase	:		str    =   k_proj
       lowercase	:		List[Any]    =   k_proj_bias

       lowercase	:		Dict    =   v_proj
       lowercase	:		List[str]    =   v_proj_bias

       lowercase	:		Optional[int]    =   out_proj_weights
       lowercase	:		Tuple    =   out_proj_bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_linear.weight
       lowercase	:		Tuple    =   pt_linear.bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''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 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.token_embedding.weight
       lowercase	:		Optional[int]    =   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 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.text_projection.data.T

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




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

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Dict    =   flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']   )

       lowercase	:		Union[str, Any]    =   torch.from_numpy(flax_class_token_params['''scale''']   )
       lowercase	:		List[str]    =   torch.from_numpy(flax_class_token_params['''bias''']   )
       lowercase	:		Tuple    =   nn.Parameter(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   nn.Parameter(lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   hf_model.state_dict()
       lowercase	:		Dict    =   {}

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

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

              if "weight" in torch_key and v.ndim == 2:
                     lowercase	:		Optional[int]    =   v.T

              lowercase	:		Optional[int]    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

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

       for flax_key, v in flax_box_params.items():
              lowercase	:		Dict    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Union[str, Any]    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )
              lowercase	:		int    =   torch_key.replace('''_''' ,					''''''   ).lower()
              lowercase	:		Union[str, Any]    =   '''box_head.''' + torch_key

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

              lowercase	:		Tuple    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




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

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

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

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

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

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




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   {}
       lowercase	:		Union[str, Any]    =   []

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

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




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		Tuple    =   CLIP(**lowerCamelCase   )
       torch_model.eval()
       lowercase	:		Any    =   torch_model.state_dict()

       lowercase	:		List[Any]    =   flatten_nested_dict(flax_params['''backbone''']['''clip''']   )
       lowercase	:		str    =   {}

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

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

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     lowercase	:		List[str]    =   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	:		Union[str, Any]    =   v.T

              lowercase	:		str    =   v

       lowercase	:		Tuple    =   _convert_attn_layers(lowerCamelCase   )
       new_torch_params.update(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   {}

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

       return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase=None   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   Repository(lowerCamelCase ,					clone_from=f"""google/{pytorch_dump_folder_path}"""   )
       repo.git_pull()

       if config_path is not None:
              lowercase	:		Any    =   OwlViTConfig.from_pretrained(lowerCamelCase   )
       else:
              lowercase	:		Optional[int]    =   OwlViTConfig()

       lowercase	:		Any    =   OwlViTModel(lowerCamelCase   ).eval()
       lowercase	:		str    =   OwlViTForObjectDetection(lowerCamelCase   ).eval()

       copy_text_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       copy_vision_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		Any    =   pt_backbone.logit_scale
       copy_flax_attn_params(lowerCamelCase ,					lowerCamelCase   )

       lowercase	:		Optional[Any]    =   hf_backbone
       copy_class_merge_token(lowerCamelCase ,					lowerCamelCase   )
       copy_class_box_heads(lowerCamelCase ,					lowerCamelCase   )

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

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

       # Initialize processor
       lowercase	:		Tuple    =   OwlViTProcessor(image_processor=lowerCamelCase ,					tokenizer=lowerCamelCase   )
       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__":
      lowercase						:   Optional[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.'''
      )
      lowercase						:   Optional[int]      						=							parser.parse_args()

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

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

      # Convert CLIP backbone
      lowercase						,						lowercase						,						lowercase						:   Optional[int]      						=							convert_clip_backbone(flax_params, torch_config)

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




