


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class        a	:




	"""simple docstring"""


	def __init__(							self :						Optional[int]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						List[Any]=3					,			lowerCamelCase :						List[Any]=32					,			lowerCamelCase :						Optional[int]=3					,			lowerCamelCase :						str=10					,			lowerCamelCase :						Optional[Any]=[10, 20, 30, 40]					,			lowerCamelCase :						Optional[Any]=[1, 1, 2, 1]					,			lowerCamelCase :						Tuple=True					,			lowerCamelCase :						int=True					,			lowerCamelCase :						Any="relu"					,			lowerCamelCase :						Union[str, Any]=3					,			lowerCamelCase :						List[Any]=None					,			)			->					List[str]:
								__snake_case	:    Optional[Any]											=     parent
								__snake_case	:    List[Any]											=     batch_size
								__snake_case	:    Tuple											=     image_size
								__snake_case	:    Dict											=     num_channels
								__snake_case	:    int											=     embeddings_size
								__snake_case	:    str											=     hidden_sizes
								__snake_case	:    Dict											=     depths
								__snake_case	:    Union[str, Any]											=     is_training
								__snake_case	:    Optional[int]											=     use_labels
								__snake_case	:    str											=     hidden_act
								__snake_case	:    Optional[int]											=     num_labels
								__snake_case	:    Optional[int]											=     scope
								__snake_case	:    List[Any]											=     len(_A       )

	def 					__snake_case      (							self :						List[str]       )			->					List[str]:
								__snake_case	:    List[str]											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								__snake_case	:    Tuple											=     None
								if self.use_labels:
															__snake_case	:    Any											=     ids_tensor([self.batch_size]					,			self.num_labels       )

								__snake_case	:    Dict											=     self.get_config()
								return config, pixel_values, labels

	def 					__snake_case      (							self :						str       )			->					Tuple:
								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					,			)

	def 					__snake_case      (							self :						str					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						str       )			->					Tuple:
								__snake_case	:    List[Any]											=     TFRegNetModel(config=_A       )
								__snake_case	:    Union[str, Any]											=     model(_A					,			training=_A       )
								# expected last hidden states: B, C, H // 32, W // 32
								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 :						List[str]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						str					,			lowerCamelCase :						List[str]       )			->					int:
								__snake_case	:    str											=     self.num_labels
								__snake_case	:    List[str]											=     TFRegNetForImageClassification(_A       )
								__snake_case	:    Optional[int]											=     model(_A					,			labels=_A					,			training=_A       )
								self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)       )






	def 					__snake_case      (							self :						Dict       )			->					Union[str, Any]:
								__snake_case	:    Optional[int]											=     self.prepare_config_and_inputs()
								__snake_case				,    __snake_case				,    __snake_case	:    str											=     config_and_inputs
								__snake_case	:    Any											=     {"pixel_values": pixel_values}
								return config, inputs_dict

@require_tf
class        a	(_a  ,       _a  ,       unittest.TestCase   ):




	"""simple docstring"""


	__UpperCAmelCase					:							Optional[Any]  						=						(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
	__UpperCAmelCase					:							List[str]  						=						(
	    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	__UpperCAmelCase					:							Union[str, Any]  						=						False
	__UpperCAmelCase					:							str  						=						False
	__UpperCAmelCase					:							str  						=						False
	__UpperCAmelCase					:							str  						=						False
	__UpperCAmelCase					:							str  						=						False

	def 					__snake_case      (							self :						Dict       )			->					str:
								__snake_case	:    int											=     TFRegNetModelTester(self       )
								__snake_case	:    int											=     ConfigTester(self					,			config_class=_A					,			has_text_modality=_A       )

	def 					__snake_case      (							self :						Tuple       )			->					Any:
								return

	@unittest.skip(reason="RegNet does not use inputs_embeds"       )
	def 					__snake_case      (							self :						str       )			->					Tuple:
								pass

	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0					,			reason="TF does not support backprop for grouped convolutions on CPU."					,			)
	@slow
	def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[int]:
								super().test_keras_fit()

	@unittest.skip(reason="RegNet does not support input and output embeddings"       )
	def 					__snake_case      (							self :						str       )			->					str:
								pass

	def 					__snake_case      (							self :						Tuple       )			->					int:
								__snake_case				,    __snake_case	:    str											=     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__snake_case	:    int											=     model_class(_A       )
															__snake_case	:    Union[str, Any]											=     inspect.signature(model.call       )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__snake_case	:    Dict											=     [*signature.parameters.keys()]

															__snake_case	:    List[Any]											=     ["pixel_values"]
															self.assertListEqual(arg_names[:1]					,			_A       )

	def 					__snake_case      (							self :						int       )			->					Tuple:
								__snake_case	:    Tuple											=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A       )

	def 					__snake_case      (							self :						Dict       )			->					List[Any]:
								def check_hidden_states_output(lowerCamelCase :						List[str]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						str       ):
															__snake_case	:    Union[str, Any]											=     model_class(_A       )
															__snake_case	:    Optional[Any]											=     model(**self._prepare_for_class(_A					,			_A       )					,			training=_A       )

															__snake_case	:    List[str]											=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__snake_case	:    Tuple											=     self.model_tester.num_stages
															self.assertEqual(len(_A       )					,			expected_num_stages + 1       )

															# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]       )					,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,			)

								__snake_case				,    __snake_case	:    Union[str, Any]											=     self.model_tester.prepare_config_and_inputs_for_common()
								__snake_case	:    List[Any]											=     ["basic", "bottleneck"]
								for model_class in self.all_model_classes:
															for layer_type in layers_type:
																						__snake_case	:    str											=     layer_type
																						__snake_case	:    Dict											=     True
																						check_hidden_states_output(_A					,			_A					,			_A       )

																						# check that output_hidden_states also work using config
																						del inputs_dict["output_hidden_states"]
																						__snake_case	:    Union[str, Any]											=     True

																						check_hidden_states_output(_A					,			_A					,			_A       )

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

								def check_equivalence(lowerCamelCase :						Any					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Dict={}       ):
															__snake_case	:    Any											=     model(_A					,			return_dict=_A					,			**_A       )
															__snake_case	:    Any											=     model(_A					,			return_dict=_A					,			**_A       ).to_tuple()

															def recursive_check(lowerCamelCase :						List[str]					,			lowerCamelCase :						int       ):
																						if isinstance(_A					,			(List, Tuple)       ):
																													for tuple_iterable_value, dict_iterable_value in zip(_A					,			_A       ):
																																				recursive_check(_A					,			_A       )
																						elif tuple_object is None:
																													return
																						else:
																													self.assertTrue(
																													    all(tf.equal(_A					,			_A       )       )					,			msg=(
																													        "Tuple and dict output are not equal. Difference:"
																													        F' {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}'
																													    )					,			)

																						recursive_check(_A					,			_A       )

								for model_class in self.all_model_classes:
															__snake_case	:    Optional[int]											=     model_class(_A       )

															__snake_case	:    List[Any]											=     self._prepare_for_class(_A					,			_A       )
															__snake_case	:    int											=     self._prepare_for_class(_A					,			_A       )
															check_equivalence(_A					,			_A					,			_A       )

															__snake_case	:    List[str]											=     self._prepare_for_class(_A					,			_A					,			return_labels=_A       )
															__snake_case	:    Union[str, Any]											=     self._prepare_for_class(_A					,			_A					,			return_labels=_A       )
															check_equivalence(_A					,			_A					,			_A       )

															__snake_case	:    List[Any]											=     self._prepare_for_class(_A					,			_A       )
															__snake_case	:    Optional[int]											=     self._prepare_for_class(_A					,			_A       )
															check_equivalence(_A					,			_A					,			_A					,			{"output_hidden_states": True}       )

															__snake_case	:    List[str]											=     self._prepare_for_class(_A					,			_A					,			return_labels=_A       )
															__snake_case	:    str											=     self._prepare_for_class(_A					,			_A					,			return_labels=_A       )
															check_equivalence(_A					,			_A					,			_A					,			{"output_hidden_states": True}       )

	def 					__snake_case      (							self :						str       )			->					int:
								__snake_case	:    Tuple											=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_A       )






	@slow
	def 					__snake_case      (							self :						Optional[Any]       )			->					List[str]:
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__snake_case	:    List[str]											=     TFRegNetModel.from_pretrained(_A       )
															self.assertIsNotNone(_A       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    List[str]											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

@require_tf
@require_vision
class        a	(unittest.TestCase   ):




	"""simple docstring"""


	@cached_property
	def 					__snake_case      (							self :						int       )			->					Tuple:
								return (
								    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
								    if is_vision_available()
								    else None
								)






	@slow
	def 					__snake_case      (							self :						int       )			->					List[str]:
								__snake_case	:    int											=     TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

								__snake_case	:    Optional[int]											=     self.default_image_processor
								__snake_case	:    Optional[int]											=     prepare_img()
								__snake_case	:    Any											=     image_processor(images=_A					,			return_tensors="tf"       )

								# forward pass
								__snake_case	:    List[str]											=     model(**_A					,			training=_A       )

								# verify the logits
								__snake_case	:    Dict											=     tf.TensorShape((1, 1000)       )
								self.assertEqual(outputs.logits.shape					,			_A       )

								__snake_case	:    Tuple											=     tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

								tf.debugging.assert_near(outputs.logits[0, :3]					,			_A					,			atol=1E-4       )





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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",
}

_snake_case  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    Any											=     create_model(
							    "HTSAT-tiny"							,			"roberta"							,			__lowerCamelCase							,			precision="fp32"							,			device="cuda:0" if torch.cuda.is_available() else "cpu"							,			enable_fusion=__lowerCamelCase							,			fusion_type="aff_2d" if enable_fusion else None							,			)
							return model, model_cfg







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    Any											=     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:
																												__snake_case	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

														if re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					# replace sequential layers with list
																					__snake_case	:    List[Any]											=     re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)

																					__snake_case	:    str											=     key.replace(F'sequential.{sequential_layer}.'							,			F'layers.{int(__lowerCamelCase			)//3}.linear.'			)
														elif re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Any											=     int(re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)			)

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

																					__snake_case	:    Tuple											=     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
																					__snake_case	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     ClapModel(__lowerCamelCase			)

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

							model.save_pretrained(__lowerCamelCase			)
							transformers_config.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
			_snake_case  :	Union[str, 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")
			_snake_case  :	Tuple			      =       parser.parse_args()

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

