





"""simple docstring"""





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp
    import numpy as np

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
    import torch





@require_flax
class 			snake_case		:

      def __init__(			self   :Optional[Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Dict=1_4						,							_lowerCamelCase   :Any=7						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :str=True						,							_lowerCamelCase   :Union[str, Any]=True						,							_lowerCamelCase   :Optional[int]=9_9						,							_lowerCamelCase   :List[str]=3_2						,							_lowerCamelCase   :List[str]=5						,							_lowerCamelCase   :Tuple=4						,							_lowerCamelCase   :Tuple=3_7						,							_lowerCamelCase   :Optional[int]="gelu"						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :Optional[int]=0.1						,							_lowerCamelCase   :int=5_1_2						,							_lowerCamelCase   :str=0.0_2						,							_lowerCamelCase   :Dict=None						,							):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       parent
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       batch_size
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length
       __SCREAMING_SNAKE_CASE :			Dict												=       is_training
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       use_input_mask
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_labels
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       d_model
       __SCREAMING_SNAKE_CASE :			Dict												=       num_hidden_layers
       __SCREAMING_SNAKE_CASE :			List[Any]												=       num_attention_heads
       __SCREAMING_SNAKE_CASE :			int												=       ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       activation_function
       __SCREAMING_SNAKE_CASE :			int												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			List[str]												=       initializer_range
       __SCREAMING_SNAKE_CASE :			str												=       None
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       0
       __SCREAMING_SNAKE_CASE :			Any												=       2
       __SCREAMING_SNAKE_CASE :			Tuple												=       1

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Any												=       np.clip(ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size						)						,							3						,							self.vocab_size						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       None
       if self.use_input_mask:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       random_attention_mask([self.batch_size, self.seq_length]						)

       __SCREAMING_SNAKE_CASE :			str												=       XGLMConfig(
           vocab_size=self.vocab_size						,							d_model=self.hidden_size						,							num_layers=self.num_hidden_layers						,							attention_heads=self.num_attention_heads						,							ffn_dim=self.ffn_dim						,							activation_function=self.activation_function						,							activation_dropout=self.activation_dropout						,							attention_dropout=self.attention_dropout						,							max_position_embeddings=self.max_position_embeddings						,							initializer_range=self.initializer_range						,							use_cache=_lowerCamelCase						,							bos_token_id=self.bos_token_id						,							eos_token_id=self.eos_token_id						,							pad_token_id=self.pad_token_id						,							)

       return (config, input_ids, input_mask)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			int												=       self.prepare_config_and_inputs()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       config_and_inputs
       __SCREAMING_SNAKE_CASE :			int												=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       self.prepare_config_and_inputs()

       __SCREAMING_SNAKE_CASE :			List[str]												=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       __SCREAMING_SNAKE_CASE :			int												=       ids_tensor([self.batch_size, self.seq_length]						,							vocab_size=2						)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       2_0
       __SCREAMING_SNAKE_CASE :			int												=       model_class_name(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model.init_cache(input_ids.shape[0]						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       jnp.ones((input_ids.shape[0], max_decoder_length)						,							dtype='''i4'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)
       __SCREAMING_SNAKE_CASE :			int												=       model(
           input_ids[:, :-1]						,							attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			Any												=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,							dtype='''i4'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(
           input_ids[:, -1:]						,							attention_mask=_lowerCamelCase						,							past_key_values=outputs_cache.past_key_values						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Any												=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1e-3						,							msg=f'''Max diff is {diff}'''						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       2_0
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model_class_name(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Any												=       jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]						,							axis=-1						,							)

       __SCREAMING_SNAKE_CASE :			List[str]												=       model.init_cache(input_ids.shape[0]						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)

       __SCREAMING_SNAKE_CASE :			Dict												=       model(
           input_ids[:, :-1]						,							attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			int												=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,							dtype='''i4'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(
           input_ids[:, -1:]						,							past_key_values=outputs_cache.past_key_values						,							attention_mask=_lowerCamelCase						,							position_ids=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			Tuple												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
       self.parent.assertTrue(diff < 1e-3						,							msg=f'''Max diff is {diff}'''						)







@require_sentencepiece
@require_flax
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
      lowerCamelCase__						= (FlaxXGLMForCausalLM,) if is_flax_available() else ()

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       FlaxXGLMModelTester(self						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			int												=       XGLMTokenizer.from_pretrained('''XGLM'''						,							padding_side='''left'''						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       tokenizer(['''Hello this is a long string''', '''Hey''']						,							return_tensors='''np'''						,							padding=_lowerCamelCase						,							truncation=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
       __SCREAMING_SNAKE_CASE :			Any												=       1
       __SCREAMING_SNAKE_CASE :			int												=       False

       __SCREAMING_SNAKE_CASE :			int												=       jax.jit(model.generate						)

       __SCREAMING_SNAKE_CASE :			str												=       jit_generate(inputs['''input_ids''']						,							attention_mask=inputs['''attention_mask''']						).sequences

       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.batch_decode(_lowerCamelCase						,							skip_special_tokens=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       [
           '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
           '''Hey, I\'m a newbie to the forum and I\'''',
       ]

       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

      @is_pt_flax_cross_test
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__						):
         # prepare inputs
         __SCREAMING_SNAKE_CASE :			str												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			Optional[int]												=       {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         __SCREAMING_SNAKE_CASE :			List[str]												=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       getattr(_lowerCamelCase						,							_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_inputs['''input_ids'''].shape
         __SCREAMING_SNAKE_CASE :			Any												=       np.random.randint(0						,							seq_length - 1						,							size=(batch_size,)						)
         for batch_idx, start_index in enumerate(_lowerCamelCase						):
          __SCREAMING_SNAKE_CASE :			int												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
          __SCREAMING_SNAKE_CASE :			Tuple												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
         __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_model_class(_lowerCamelCase						).eval()
         # Flax models don't use the `use_cache` option and cache is not returned as a default.
         # So we disable `use_cache` here for PyTorch model.
         __SCREAMING_SNAKE_CASE :			str												=       False
         __SCREAMING_SNAKE_CASE :			List[str]												=       model_class(_lowerCamelCase						,							dtype=jnp.floataa						)
         __SCREAMING_SNAKE_CASE :			str												=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			List[str]												=       fx_state

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			Dict												=       pt_model(**_lowerCamelCase						).to_tuple()

         __SCREAMING_SNAKE_CASE :			Optional[Any]												=       fx_model(**_lowerCamelCase						).to_tuple()
         self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

         with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(_lowerCamelCase						)
          __SCREAMING_SNAKE_CASE :			Any												=       model_class.from_pretrained(_lowerCamelCase						,							from_pt=_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE :			Dict												=       fx_model_loaded(**_lowerCamelCase						).to_tuple()
         self.assertEqual(
             len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output_loaded, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output_loaded[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

      @is_pt_flax_cross_test
      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__						):
         # prepare inputs
         __SCREAMING_SNAKE_CASE :			List[str]												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)
         __SCREAMING_SNAKE_CASE :			Dict												=       {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         __SCREAMING_SNAKE_CASE :			Any												=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __SCREAMING_SNAKE_CASE :			int												=       getattr(_lowerCamelCase						,							_lowerCamelCase						)

         __SCREAMING_SNAKE_CASE :			str												=       pt_model_class(_lowerCamelCase						).eval()
         __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       False
         __SCREAMING_SNAKE_CASE :			int												=       model_class(_lowerCamelCase						,							dtype=jnp.floataa						)

         __SCREAMING_SNAKE_CASE :			Dict												=       load_flax_weights_in_pytorch_model(_lowerCamelCase						,							fx_model.params						)
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_inputs['''input_ids'''].shape
         __SCREAMING_SNAKE_CASE :			Dict												=       np.random.randint(0						,							seq_length - 1						,							size=(batch_size,)						)
         for batch_idx, start_index in enumerate(_lowerCamelCase						):
          __SCREAMING_SNAKE_CASE :			List[Any]												=       0
          __SCREAMING_SNAKE_CASE :			str												=       1
          __SCREAMING_SNAKE_CASE :			List[Any]												=       0
          __SCREAMING_SNAKE_CASE :			Dict												=       1

         # make sure weights are tied in PyTorch
         pt_model.tie_weights()

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			Any												=       pt_model(**_lowerCamelCase						).to_tuple()

         __SCREAMING_SNAKE_CASE :			Any												=       fx_model(**_lowerCamelCase						).to_tuple()
         self.assertEqual(len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)

         with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(_lowerCamelCase						)
          __SCREAMING_SNAKE_CASE :			List[str]												=       pt_model_class.from_pretrained(_lowerCamelCase						,							from_flax=_lowerCamelCase						)

         with torch.no_grad():
          __SCREAMING_SNAKE_CASE :			List[str]												=       pt_model_loaded(**_lowerCamelCase						).to_tuple()

         self.assertEqual(
             len(_lowerCamelCase						)						,							len(_lowerCamelCase						)						,							'''Output lengths differ between Flax and PyTorch'''						)
         for fx_output, pt_output in zip(_lowerCamelCase						,							_lowerCamelCase						):
          self.assert_almost_equals(fx_output[:, -1]						,							pt_output[:, -1].numpy()						,							4e-2						)



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       for model_class_name in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			int												=       model_class_name.from_pretrained('''facebook/xglm-564M'''						)
        __SCREAMING_SNAKE_CASE :			Dict												=       model(np.ones((1, 1)						)						)
        self.assertIsNotNone(_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,
)


_lowerCamelCase       =	{
    '''vit_b32''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 32,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 16,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_68,
        '''image_resolution''': 8_40,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 24,
        '''vision_width''': 10_24,
        '''vision_patch_size''': 14,
        '''transformer_width''': 7_68,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}




def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							List[Any]="" ,      lowercase_   :							List[Any]="/"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

 for k, v in params.items():
  __SCREAMING_SNAKE_CASE :			List[str]												=       parent_key + sep + k if parent_key else k

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




def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE :			Any												=       pt_attn_layer.out_proj.weight
 __SCREAMING_SNAKE_CASE :			str												=       pt_attn_layer.out_proj.bias

 __SCREAMING_SNAKE_CASE :			Any												=       q_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       q_proj_bias

 __SCREAMING_SNAKE_CASE :			Any												=       k_proj
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       k_proj_bias

 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj_bias

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       out_proj_weights
 __SCREAMING_SNAKE_CASE :			int												=       out_proj_bias




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							Dict		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_linear.weight
 __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_linear.bias




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Any		):

 '''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_	(  lowercase_   :							List[Any] ,      lowercase_   :							Any		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.token_embedding.weight
 __SCREAMING_SNAKE_CASE :			str												=       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_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.text_projection.data.T

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




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			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
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.conva.weight.data
 __SCREAMING_SNAKE_CASE :			Tuple												=       pt_model.visual.class_embedding
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.positional_embedding.data

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




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']		)

 __SCREAMING_SNAKE_CASE :			Dict												=       torch.from_numpy(flax_class_token_params['''scale''']		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(flax_class_token_params['''bias''']		)
 __SCREAMING_SNAKE_CASE :			Dict												=       nn.Parameter(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Parameter(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       hf_model.state_dict()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

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

 for flax_key, v in flax_class_params.items():
  __SCREAMING_SNAKE_CASE :			str												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Any												=       torch_key.replace('''Dense_0''' ,      '''dense0'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       '''class_head.''' + torch_key

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

  __SCREAMING_SNAKE_CASE :			List[str]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

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

 for flax_key, v in flax_box_params.items():
  __SCREAMING_SNAKE_CASE :			Tuple												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''_''' ,      ''''''		).lower()
  __SCREAMING_SNAKE_CASE :			List[str]												=       '''box_head.''' + torch_key

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

  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






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

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

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

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

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

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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       {}
 __SCREAMING_SNAKE_CASE :			int												=       []

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

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




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       CLIP(**lowercase_		)
 torch_model.eval()
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch_model.state_dict()

 __SCREAMING_SNAKE_CASE :			str												=       flatten_nested_dict(flax_params['''backbone''']['''clip''']		)
 __SCREAMING_SNAKE_CASE :			str												=       {}

 for flax_key, v in flax_clip_params.items():
  __SCREAMING_SNAKE_CASE :			int												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       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'''		)
  ):
   __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key[5:]

  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''text_projection.kernel''' ,      '''text_projection'''		)
  __SCREAMING_SNAKE_CASE :			str												=       torch_key.replace('''visual.proj.kernel''' ,      '''visual.proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''.scale''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       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
   __SCREAMING_SNAKE_CASE :			str												=       v.T

  __SCREAMING_SNAKE_CASE :			Any												=       v

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       _convert_attn_layers(lowercase_		)
 new_torch_params.update(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

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

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							str ,      lowercase_   :							Optional[Any] ,      lowercase_   :							List[Any]=None		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       Repository(lowercase_ ,      clone_from=F'''google/{pytorch_dump_folder_path}'''		)
 repo.git_pull()

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

 __SCREAMING_SNAKE_CASE :			List[str]												=       OwlViTModel(lowercase_		).eval()
 __SCREAMING_SNAKE_CASE :			Tuple												=       OwlViTForObjectDetection(lowercase_		).eval()

 copy_text_model_and_projection(lowercase_ ,      lowercase_		)
 copy_vision_model_and_projection(lowercase_ ,      lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       hf_backbone
 copy_class_merge_token(lowercase_ ,      lowercase_		)
 copy_class_box_heads(lowercase_ ,      lowercase_		)

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

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

 # Initialize processor
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       OwlViTProcessor(image_processor=lowercase_ ,      tokenizer=lowercase_		)
 image_processor.save_pretrained(repo.local_dir		)
 processor.save_pretrained(repo.local_dir		)

 repo.git_add()
 repo.git_commit('''Upload model and processor'''		)
 repo.git_push()


if __name__ == "__main__":
    _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     ,		_lowerCamelCase     ,		_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)

