





'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def __init__(				self  :						Dict			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Optional[Any]=3			,					lowerCAmelCase__  :						str=32			,					lowerCAmelCase__  :						Tuple=3			,					lowerCAmelCase__  :						str=10			,					lowerCAmelCase__  :						Optional[Any]=[10, 20, 30, 40]			,					lowerCAmelCase__  :						Optional[Any]=[1, 1, 2, 1]			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						int="relu"			,					lowerCAmelCase__  :						str=3			,					lowerCAmelCase__  :						Dict=None			,					)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			embeddings_size
            _UpperCamelCase							       =			hidden_sizes
            _UpperCamelCase							       =			depths
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			scope
            _UpperCamelCase							       =			len(lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

            _UpperCamelCase							       =			self.get_config()

            return config, pixel_values





       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            return RegNetConfig(
                num_channels=self.num_channels			,					embeddings_size=self.embeddings_size			,					hidden_sizes=self.hidden_sizes			,					depths=self.depths			,					hidden_act=self.hidden_act			,					num_labels=self.num_labels			,					image_size=self.image_size			,					)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModel(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,					)





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			FlaxRegNetForImageClassification(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)	)







       def snake_case__		(				self  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.prepare_config_and_inputs()
            _UpperCamelCase       ,							_UpperCamelCase							       =			config_and_inputs
            _UpperCamelCase							       =			{'''pixel_values''': pixel_values}
            return config, inputs_dict

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       _snake_case      :	Any									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						str	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





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

            '''simple docstring'''




            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()





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

            '''simple docstring'''




            return





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)





       @unittest.skip(reason='''RegNet does not use inputs_embeds'''	)
       def snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''RegNet does not support input and output embeddings'''	)
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Dict	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                 _UpperCamelCase							       =			inspect.signature(model.__call__	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _UpperCamelCase							       =			[*signature.parameters.keys()]

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]	):
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                 _UpperCamelCase							       =			model(**self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)	)

                 _UpperCamelCase							       =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 _UpperCamelCase							       =			self.model_tester.num_stages
                 self.assertEqual(len(lowerCAmelCase__	)			,					expected_num_stages + 1	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			True
                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _UpperCamelCase							       =			True

                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)







       def snake_case__		(				self  :						Dict	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def model_jitted(lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	):
                           return model(pixel_values=lowerCAmelCase__			,					**lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)







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





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''	) if is_vision_available() else None







       @slow
       def snake_case__		(				self  :						Tuple	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	)

            _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            # verify the logits
            _UpperCamelCase							       =			(1, 1000)
            self.assertEqual(outputs.logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			jnp.array([-0.4180, -1.5051, -3.4836]	)

            self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    # vision encoder
    if "img_encoder.pos_embed" in name:
        snake_case__    =					name.replace("img_encoder.pos_embed"     ,		"vision_model.embeddings.position_embeddings"				)
    if "img_encoder.patch_embed.proj" in name:
        snake_case__    =					name.replace("img_encoder.patch_embed.proj"     ,		"vision_model.embeddings.patch_embeddings.projection"				)
    if "img_encoder.patch_embed.norm" in name:
        snake_case__    =					name.replace("img_encoder.patch_embed.norm"     ,		"vision_model.embeddings.layernorm"				)
    if "img_encoder.layers" in name:
        snake_case__    =					name.replace("img_encoder.layers"     ,		"vision_model.encoder.stages"				)
    if "blocks" in name and "res" not in name:
        snake_case__    =					name.replace("blocks"     ,		"layers"				)
    if "attn" in name and "pre_assign" not in name:
        snake_case__    =					name.replace("attn"     ,		"self_attn"				)
    if "proj" in name and "self_attn" in name and "text" not in name:
        snake_case__    =					name.replace("proj"     ,		"out_proj"				)
    if "pre_assign_attn.attn.proj" in name:
        snake_case__    =					name.replace("pre_assign_attn.attn.proj"     ,		"pre_assign_attn.attn.out_proj"				)
    if "norm1" in name:
        snake_case__    =					name.replace("norm1"     ,		"layer_norm1"				)
    if "norm2" in name and "pre_assign" not in name:
        snake_case__    =					name.replace("norm2"     ,		"layer_norm2"				)
    if "img_encoder.norm" in name:
        snake_case__    =					name.replace("img_encoder.norm"     ,		"vision_model.layernorm"				)
    # text encoder
    if "text_encoder.token_embedding" in name:
        snake_case__    =					name.replace("text_encoder.token_embedding"     ,		"text_model.embeddings.token_embedding"				)
    if "text_encoder.positional_embedding" in name:
        snake_case__    =					name.replace("text_encoder.positional_embedding"     ,		"text_model.embeddings.position_embedding.weight"				)
    if "text_encoder.transformer.resblocks." in name:
        snake_case__    =					name.replace("text_encoder.transformer.resblocks."     ,		"text_model.encoder.layers."				)
    if "ln_1" in name:
        snake_case__    =					name.replace("ln_1"     ,		"layer_norm1"				)
    if "ln_2" in name:
        snake_case__    =					name.replace("ln_2"     ,		"layer_norm2"				)
    if "c_fc" in name:
        snake_case__    =					name.replace("c_fc"     ,		"fc1"				)
    if "c_proj" in name:
        snake_case__    =					name.replace("c_proj"     ,		"fc2"				)
    if "text_encoder" in name:
        snake_case__    =					name.replace("text_encoder"     ,		"text_model"				)
    if "ln_final" in name:
        snake_case__    =					name.replace("ln_final"     ,		"final_layer_norm"				)
    # projection layers
    if "img_projector.linear_hidden." in name:
        snake_case__    =					name.replace("img_projector.linear_hidden."     ,		"visual_projection."				)
    if "img_projector.linear_out." in name:
        snake_case__    =					name.replace("img_projector.linear_out."     ,		"visual_projection.3."				)
    if "text_projector.linear_hidden" in name:
        snake_case__    =					name.replace("text_projector.linear_hidden"     ,		"text_projection"				)
    if "text_projector.linear_out" in name:
        snake_case__    =					name.replace("text_projector.linear_out"     ,		"text_projection.3"				)

    return name





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    for key in orig_state_dict.copy().keys():
        snake_case__    =					orig_state_dict.pop(__lowerCAmelCase				)

        if "qkv" in key:
            # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            snake_case__    =					key.split("."				)
            snake_case__    ,       snake_case__    =					int(key_split[2]				), int(key_split[4]				)
            snake_case__    =					config.vision_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[dim : dim * 2, :]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					val[-dim:]
        elif "in_proj" in key:
            # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            snake_case__    =					key.split("."				)
            snake_case__    =					int(key_split[3]				)
            snake_case__    =					config.text_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[
                    dim : dim * 2, :
                ]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					val[-dim:]
        else:
            snake_case__    =					rename_key(__lowerCAmelCase				)
            # squeeze if necessary
            if (
                "text_projection.0" in new_name
                or "text_projection.3" in new_name
                or "visual_projection.0" in new_name
                or "visual_projection.3" in new_name
            ):
                snake_case__    =					val.squeeze_()
            else:
                snake_case__    =					val

    return orig_state_dict





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case__    =					Image.open(requests.get(__lowerCAmelCase     ,		stream=__lowerCAmelCase				).raw				)
    return im





@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase="groupvit-gcc-yfcc"     ,		__lowerCAmelCase=False				):
    snake_case__    =					GroupViTConfig()
    snake_case__    =					GroupViTModel(__lowerCAmelCase				).eval()

    snake_case__    =					torch.load(__lowerCAmelCase     ,		map_location="cpu"				)["model"]
    snake_case__    =					convert_state_dict(__lowerCAmelCase     ,		__lowerCAmelCase				)
    snake_case__    ,       snake_case__    =					model.load_state_dict(__lowerCAmelCase     ,		strict=__lowerCAmelCase				)
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowerCAmelCase				) == 0)

    # verify result
    snake_case__    =					CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"				)
    snake_case__    =					prepare_img()
    snake_case__    =					processor(text=["a photo of a cat", "a photo of a dog"]     ,		images=__lowerCAmelCase     ,		padding=__lowerCAmelCase     ,		return_tensors="pt"				)

    with torch.no_grad():
        snake_case__    =					model(**__lowerCAmelCase				)

    if model_name == "groupvit-gcc-yfcc":
        snake_case__    =					torch.tensor([[13.3_523, 6.3_629]]				)
    elif model_name == "groupvit-gcc-redcaps":
        snake_case__    =					torch.tensor([[16.1_873, 8.6_230]]				)
    else:
        raise ValueError(F"""Model name {model_name} not supported."""				)
    assert torch.allclose(outputs.logits_per_image     ,		__lowerCAmelCase     ,		atol=1E-3				)

    processor.save_pretrained(__lowerCAmelCase				)
    model.save_pretrained(__lowerCAmelCase				)
    print("Successfully saved processor and model to"     ,		__lowerCAmelCase				)

    if push_to_hub:
        print("Pushing to the hub..."				)
        processor.push_to_hub(__lowerCAmelCase     ,		organization="nielsr"				)
        model.push_to_hub(__lowerCAmelCase     ,		organization="nielsr"				)


if __name__ == "__main__":
     __magic_name__  							=	argparse.ArgumentParser()
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
     )
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
     parser.add_argument(
         '''--model_name''',
         default='''groupvit-gccy-fcc''',
         type=str,
         help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
         help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
     )
     __magic_name__  							=	parser.parse_args()

     convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



