

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 EfficientNetForImageClassification, EfficientNetModel
							from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor







class 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=13						,							__snake_case=32						,							__snake_case=3						,							__snake_case=[3, 3, 5]						,							__snake_case=[32, 16, 24]						,							__snake_case=[16, 24, 40]						,							__snake_case=[1, 1, 2]						,							__snake_case=[1, 1, 2]						,							__snake_case=[1, 6, 6]						,							__snake_case=True						,							__snake_case=True						,							__snake_case=37						,							__snake_case="gelu"						,							__snake_case=10						,							):
			_UpperCamelCase :		str								=    parent
			_UpperCamelCase :		List[Any]								=    batch_size
			_UpperCamelCase :		List[str]								=    image_size
			_UpperCamelCase :		Union[str, Any]								=    num_channels
			_UpperCamelCase :		int								=    kernel_sizes
			_UpperCamelCase :		Optional[Any]								=    in_channels
			_UpperCamelCase :		Dict								=    out_channels
			_UpperCamelCase :		Optional[int]								=    strides
			_UpperCamelCase :		str								=    num_block_repeats
			_UpperCamelCase :		List[str]								=    expand_ratios
			_UpperCamelCase :		Optional[int]								=    is_training
			_UpperCamelCase :		Union[str, Any]								=    hidden_act
			_UpperCamelCase :		Tuple								=    num_labels
			_UpperCamelCase :		Optional[Any]								=    use_labels


		def 		A__       (					self):
			_UpperCamelCase :		str								=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase :		Dict								=    None
			if self.use_labels:
				_UpperCamelCase :		str								=    ids_tensor([self.batch_size]						,							self.num_labels)

			_UpperCamelCase :		List[Any]								=    self.get_config()
			return config, pixel_values, labels


		def 		A__       (					self):
			return EfficientNetConfig(
			    num_channels=self.num_channels						,							kernel_sizes=self.kernel_sizes						,							in_channels=self.in_channels						,							out_channels=self.out_channels						,							strides=self.strides						,							num_block_repeats=self.num_block_repeats						,							expand_ratios=self.expand_ratios						,							hidden_act=self.hidden_act						,							num_labels=self.num_labels						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    EfficientNetModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		List[str]								=    model(__snake_case)
			# expected last hidden states: B, C, H // 4, W // 4
			self.parent.assertEqual(
			    result.last_hidden_state.shape						,							(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Any								=    EfficientNetForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		List[str]								=    model(__snake_case						,							labels=__snake_case)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels))






		def 		A__       (					self):
			_UpperCamelCase :		Any								=    self.prepare_config_and_inputs()
			_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Any								=    config_and_inputs
			_UpperCamelCase :		Optional[int]								=    {'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 						lowercase     (					_lowercase     ,				_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
		a__       							=	(
		    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False
		a__       							=	False


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    EfficientNetModelTester(self)
			_UpperCamelCase :		int								=    ConfigTester(
			    self						,							config_class=__snake_case						,							has_text_modality=__snake_case						,							hidden_size=37)


		def 		A__       (					self):
			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 		A__       (					self):
			return


		@unittest.skip(reason='EfficientNet does not use inputs_embeds')
		def 		A__       (					self):
			pass


		@unittest.skip(reason='EfficientNet does not support input and output embeddings')
		def 		A__       (					self):
			pass


		@unittest.skip(reason='EfficientNet does not use feedforward chunking')
		def 		A__       (					self):
			pass


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs_for_common()

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

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


		def 		A__       (					self):
			_UpperCamelCase :		int								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__       (					self):
			def check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case):
				_UpperCamelCase :		str								=    model_class(__snake_case)
				model.to(__snake_case)
				model.eval()

				with torch.no_grad():
					_UpperCamelCase :		List[Any]								=    model(**self._prepare_for_class(__snake_case						,							__snake_case))

				_UpperCamelCase :		Tuple								=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
				_UpperCamelCase :		Optional[Any]								=    sum(config.num_block_repeats) * 4
				self.assertEqual(len(__snake_case)						,							__snake_case)

				# EfficientNet'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]						,							)

			_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    self.model_tester.prepare_config_and_inputs_for_common()

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

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

				check_hidden_states_output(__snake_case						,							__snake_case						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


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


		@slow
		def 		A__       (					self):
			for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase :		Dict								=    EfficientNetModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






		@is_pipeline_test
		@require_vision
		@slow
		def 		A__       (					self):
			super().test_pipeline_image_classification()






def        lowerCamelCase_  (				)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
	return image







@require_torch
@require_vision
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		@cached_property
		def 		A__       (					self):
			return AutoImageProcessor.from_pretrained('google/efficientnet-b7') if is_vision_available() else None






		@slow
		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7').to(__snake_case)

			_UpperCamelCase :		Optional[Any]								=    self.default_image_processor
			_UpperCamelCase :		Union[str, Any]								=    prepare_img()
			_UpperCamelCase :		str								=    image_processor(images=__snake_case						,							return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase :		Union[str, Any]								=    model(**__snake_case)

			# verify the logits
			_UpperCamelCase :		Any								=    torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape						,							__snake_case)

			_UpperCamelCase :		Optional[int]								=    torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2]).to(__snake_case)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]						,							__snake_case						,							atol=1e-4))







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__             =		logging.get_logger(__name__)

lowerCAmelCase__             =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
	    F''' {value.shape} for {full_name}'''
	)

	if weight_type == "weight":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

	logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

	logger.warning(F'''Unused weights: {unused_weights}'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    full_name.split('conv_layers.'						)[-1]
	_UpperCamelCase :		str								=    name.split('.'						)
	_UpperCamelCase :		List[Any]								=    int(items[0]						)
	_UpperCamelCase :		Tuple								=    int(items[1]						)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
			)
			_UpperCamelCase :		Any								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		List[str]								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
			    " found."
			)
			_UpperCamelCase :		str								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		Dict								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
	else:
		unused_weights.append(UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




