



"""simple docstring"""


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    LevitForImageClassification,
					    LevitForImageClassificationWithTeacher,
					    LevitModel,
					)
					from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[Any]   )->Dict:
							_UpperCAmelCase					=    self.config_class(**self.inputs_dict   )
							self.parent.assertTrue(hasattr(__UpperCamelCase				,   '''hidden_sizes'''   )   )
							self.parent.assertTrue(hasattr(__UpperCamelCase				,   '''num_attention_heads'''   )   )



class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    List[Any]=1_3				,   __UpperCamelCase     :    Tuple=6_4				,   __UpperCamelCase     :    int=3				,   __UpperCamelCase     :    int=3				,   __UpperCamelCase     :    Tuple=2				,   __UpperCamelCase     :    int=1				,   __UpperCamelCase     :    Any=1_6				,   __UpperCamelCase     :    Optional[Any]=[1_2_8, 2_5_6, 3_8_4]				,   __UpperCamelCase     :    Any=[4, 6, 8]				,   __UpperCamelCase     :    Any=[2, 3, 4]				,   __UpperCamelCase     :    Optional[int]=[1_6, 1_6, 1_6]				,   __UpperCamelCase     :    List[str]=0				,   __UpperCamelCase     :    List[str]=[2, 2, 2]				,   __UpperCamelCase     :    List[Any]=[2, 2, 2]				,   __UpperCamelCase     :    Tuple=0.0_2				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    str=True				,   __UpperCamelCase     :    List[str]=2				,   )->Union[str, Any]:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    image_size
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    kernel_size
							_UpperCAmelCase					=    stride
							_UpperCAmelCase					=    padding
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    num_attention_heads
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    key_dim
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    patch_size
							_UpperCAmelCase					=    attention_ratio
							_UpperCAmelCase					=    mlp_ratio
							_UpperCAmelCase					=    initializer_range
							_UpperCAmelCase					=    [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
							_UpperCAmelCase					=    is_training
							_UpperCAmelCase					=    use_labels
							_UpperCAmelCase					=    num_labels
							_UpperCAmelCase					=    initializer_range



			def  lowercase__						(		self     :    Optional[Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							_UpperCAmelCase					=    None
							if self.use_labels:
											_UpperCAmelCase					=    ids_tensor([self.batch_size]				,   self.num_labels   )

							_UpperCAmelCase					=    self.get_config()
							return config, pixel_values, labels



			def  lowercase__						(		self     :    Tuple   )->Any:
							return LevitConfig(
							    image_size=self.image_size				,   num_channels=self.num_channels				,   kernel_size=self.kernel_size				,   stride=self.stride				,   padding=self.padding				,   patch_size=self.patch_size				,   hidden_sizes=self.hidden_sizes				,   num_attention_heads=self.num_attention_heads				,   depths=self.depths				,   key_dim=self.key_dim				,   drop_path_rate=self.drop_path_rate				,   mlp_ratio=self.mlp_ratio				,   attention_ratio=self.attention_ratio				,   initializer_range=self.initializer_range				,   down_ops=self.down_ops				,   )



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[int]   )->List[Any]:
							_UpperCAmelCase					=    LevitModel(config=__UpperCamelCase   )
							model.to(__UpperCamelCase   )
							model.eval()
							_UpperCAmelCase					=    model(__UpperCamelCase   )
							_UpperCAmelCase					=    (self.image_size, self.image_size)
							_UpperCAmelCase		,						_UpperCAmelCase					=    image_size[0], image_size[1]
							for _ in range(4   ):
											_UpperCAmelCase					=    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
											_UpperCAmelCase					=    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape				,   (self.batch_size, ceil(height / 4   ) * ceil(width / 4   ), self.hidden_sizes[-1])				,   )



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->Tuple:
							_UpperCAmelCase					=    self.num_labels
							_UpperCAmelCase					=    LevitForImageClassification(__UpperCamelCase   )
							model.to(__UpperCamelCase   )
							model.eval()
							_UpperCAmelCase					=    model(__UpperCamelCase				,   labels=__UpperCamelCase   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    self.prepare_config_and_inputs()
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    config_and_inputs
							_UpperCAmelCase					=    {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class 			_a (					lowerCAmelCase			,							lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__			      =	(
			    {
			        """feature-extraction""": LevitModel,
			        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False



			def  lowercase__						(		self     :    List[str]   )->Dict:
							_UpperCAmelCase					=    LevitModelTester(self   )
							_UpperCAmelCase					=    ConfigTester(self				,   config_class=__UpperCamelCase				,   has_text_modality=__UpperCamelCase				,   hidden_size=3_7   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							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  lowercase__						(		self     :    Union[str, Any]   )->int:
							return



			@unittest.skip(reason='''Levit does not use inputs_embeds'''   )
			def  lowercase__						(		self     :    str   )->Optional[Any]:
							pass



			@unittest.skip(reason='''Levit does not support input and output embeddings'''   )
			def  lowercase__						(		self     :    int   )->str:
							pass



			@unittest.skip(reason='''Levit does not output attentions'''   )
			def  lowercase__						(		self     :    Tuple   )->Tuple:
							pass



			def  lowercase__						(		self     :    str   )->Optional[int]:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase					=    ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->List[Any]:
							def check_hidden_states_output(__UpperCamelCase     :    int				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    int   ):
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											model.to(__UpperCamelCase   )
											model.eval()

											with torch.no_grad():
															_UpperCAmelCase					=    model(**self._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase   )   )

											_UpperCAmelCase					=    outputs.hidden_states

											_UpperCAmelCase					=    len(self.model_tester.depths   ) + 1
											self.assertEqual(len(__UpperCamelCase   )				,   __UpperCamelCase   )

											_UpperCAmelCase					=    (self.model_tester.image_size, self.model_tester.image_size)
											_UpperCAmelCase		,						_UpperCAmelCase					=    image_size[0], image_size[1]
											for _ in range(4   ):
															_UpperCAmelCase					=    floor(
															    (
															        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1   )
															_UpperCAmelCase					=    floor(
															    (
															        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1   )
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]   )				,   [
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]				,   )

							_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(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

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

											check_hidden_states_output(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
			def  lowercase__						(		self     :    str   )->int:
							pass



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Union[str, Any]=False   )->Dict:
							_UpperCAmelCase					=    super()._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase				,   return_labels=__UpperCamelCase   )

							if return_labels:
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															del inputs_dict["labels"]

							return inputs_dict



			def  lowercase__						(		self     :    List[Any]   )->List[Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]   )->List[Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple   )->List[str]:
							if not self.model_tester.is_training:
											return

							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase					=    True

							for model_class in self.all_model_classes:
											# LevitForImageClassificationWithTeacher supports inference-only
											if (
											    model_class in get_values(__UpperCamelCase   )
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
															continue
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											model.to(__UpperCamelCase   )
											model.train()
											_UpperCAmelCase					=    self._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase				,   return_labels=__UpperCamelCase   )
											_UpperCAmelCase					=    model(**__UpperCamelCase   ).loss
											loss.backward()



			def  lowercase__						(		self     :    int   )->Any:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
											return

							_UpperCAmelCase					=    False
							_UpperCAmelCase					=    True

							for model_class in self.all_model_classes:
											if model_class in get_values(__UpperCamelCase   ) or not model_class.supports_gradient_checkpointing:
															continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															continue
											_UpperCAmelCase					=    model_class(__UpperCamelCase   )
											model.gradient_checkpointing_enable()
											model.to(__UpperCamelCase   )
											model.train()
											_UpperCAmelCase					=    self._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase				,   return_labels=__UpperCamelCase   )
											_UpperCAmelCase					=    model(**__UpperCamelCase   ).loss
											loss.backward()



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase		,						_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCAmelCase					=    [
							    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
							    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
							    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
							]

							for model_class in self.all_model_classes:
											if (
											    model_class
											    not in [
											        *get_values(__UpperCamelCase   ),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
															continue

											for problem_type in problem_types:
															with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'   ):
																			_UpperCAmelCase					=    problem_type['''title''']
																			_UpperCAmelCase					=    problem_type['''num_labels''']

																			_UpperCAmelCase					=    model_class(__UpperCamelCase   )
																			model.to(__UpperCamelCase   )
																			model.train()

																			_UpperCAmelCase					=    self._prepare_for_class(__UpperCamelCase				,   __UpperCamelCase				,   return_labels=__UpperCamelCase   )

																			if problem_type["num_labels"] > 1:
																							_UpperCAmelCase					=    inputs['''labels'''].unsqueeze(1   ).repeat(1				,   problem_type['''num_labels''']   )

																			_UpperCAmelCase					=    inputs['''labels'''].to(problem_type['''dtype''']   )

																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																			# they have the same size." which is a symptom something in wrong for the regression problem.
																			# See https://github.com/huggingface/transformers/issues/11780
																			with warnings.catch_warnings(record=__UpperCamelCase   ) as warning_list:
																							_UpperCAmelCase					=    model(**__UpperCamelCase   ).loss
																			for w in warning_list:
																							if "Using a target size that is different to the input size" in str(w.message   ):
																											raise ValueError(
																											    F'Something is going wrong in the regression problem: intercepted {w.message}'   )

																			loss.backward()



			@slow
			def  lowercase__						(		self     :    List[Any]   )->Union[str, Any]:
							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase					=    LevitModel.from_pretrained(__UpperCamelCase   )
											self.assertIsNotNone(__UpperCamelCase   )







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image
@require_torch
@require_vision
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			@cached_property
			def  lowercase__						(		self     :    Optional[Any]   )->int:
							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )



			@slow
			def  lowercase__						(		self     :    str   )->Any:
							_UpperCAmelCase					=    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
							    __UpperCamelCase   )

							_UpperCAmelCase					=    self.default_image_processor
							_UpperCAmelCase					=    prepare_img()
							_UpperCAmelCase					=    image_processor(images=__UpperCamelCase				,   return_tensors='''pt'''   ).to(__UpperCamelCase   )

							# forward pass
							with torch.no_grad():
											_UpperCAmelCase					=    model(**__UpperCamelCase   )

							# verify the logits
							_UpperCAmelCase					=    torch.Size((1, 1_0_0_0)   )
							self.assertEqual(outputs.logits.shape				,   __UpperCamelCase   )

							_UpperCAmelCase					=    torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]   ).to(__UpperCamelCase   )

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








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							List[str]										=   {
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

__A   :							List[str]										=   AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    create_model(
				    '''HTSAT-tiny'''					,     '''roberta'''					,     _SCREAMING_SNAKE_CASE					,     precision='''fp32'''					,     device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,     enable_fusion=_SCREAMING_SNAKE_CASE					,     fusion_type='''aff_2d''' if enable_fusion else None					,     )
				return model, model_cfg







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    r'''.*_projection.(\d+).*'''

				for key, value in state_dict.items():
								# check if any key needs to be modified
								for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
												if key_to_modify in key:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

								if re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												# replace sequential layers with list
												_UpperCAmelCase					=    re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)

												_UpperCAmelCase					=    key.replace(f'sequential.{sequential_layer}.'					,     f'layers.{int(_SCREAMING_SNAKE_CASE				)//3}.linear.'				)
								elif re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    int(re.match(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).group(1				)				)

												# Because in CLAP they use `nn.Sequential`...
												_UpperCAmelCase					=    1 if projecton_layer == 0 else 2

												_UpperCAmelCase					=    key.replace(f'_projection.{projecton_layer}.'					,     f'_projection.linear{transformers_projection_layer}.'				)

								if "audio" and "qkv" in key:
												# split qkv into query key and value
												_UpperCAmelCase					=    value
												_UpperCAmelCase					=    mixed_qkv.size(0				) // 3

												_UpperCAmelCase					=    mixed_qkv[:qkv_dim]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim : qkv_dim * 2]
												_UpperCAmelCase					=    mixed_qkv[qkv_dim * 2 :]

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    ClapModel(_SCREAMING_SNAKE_CASE				)

				# ignore the spectrogram embedding layer
				model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)

				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				transformers_config.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Any										=   argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
					__A   :							int										=   parser.parse_args()

					convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



