


import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class     lowerCamelCase__		(							unittest.TestCase ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case=3 ,  snake_case=32 ,  snake_case=3 ,  snake_case=10 ,  snake_case=[10, 20, 30, 40] ,  snake_case=[1, 1, 2, 1] ,  snake_case=True ,  snake_case=True ,  snake_case="relu" ,  snake_case=3 ,  snake_case=None ,  ):

						'''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(snake_case       )




			def 			snake_case__			(							self       ):

						'''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       ):

						'''simple docstring'''


						return ResNetConfig(
						    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 ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    FlaxResNetModel(config=snake_case       )
						UpperCamelCase__   =    model(snake_case       )

						# 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 ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.num_labels
						UpperCamelCase__   =    FlaxResNetForImageClassification(config=snake_case       )
						UpperCamelCase__   =    model(snake_case       )
						self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)       )







			def 			snake_case__			(							self       ):

						'''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__		(							UpperCAmelCase   ,		unittest.TestCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Dict					=						(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

			_UpperCamelCase						:   Any					=						False
			_UpperCamelCase						:   int					=						False
			_UpperCamelCase						:   Tuple					=						False




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    FlaxResNetModelTester(self       )
						UpperCamelCase__   =    ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case       )




			def 			snake_case__			(							self       ):

						'''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       ):

						'''simple docstring'''


						return




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case       )




			@unittest.skip(reason="ResNet does not use inputs_embeds"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="ResNet does not support input and output embeddings"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__   =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__   =    model_class(snake_case       )
									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] ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case ,  snake_case ,  snake_case       ):
									UpperCamelCase__   =    model_class(snake_case       )

									UpperCamelCase__   =    model(**self._prepare_for_class(snake_case ,  snake_case       )       )

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

									UpperCamelCase__   =    self.model_tester.num_stages
									self.assertEqual(len(snake_case       ) ,  expected_num_stages + 1       )




			@unittest.skip(reason="ResNet does not use feedforward chunking"       )
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						pass







			def 			snake_case__			(							self       ):

						'''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(snake_case ,  snake_case       )
												UpperCamelCase__   =    model_class(snake_case       )

												@jax.jit
												def model_jitted(snake_case ,  **snake_case       ):
															return model(pixel_values=snake_case ,  **snake_case       )

												with self.subTest("JIT Enabled"       ):
															UpperCamelCase__   =    model_jitted(**snake_case       ).to_tuple()

												with self.subTest("JIT Disabled"       ):
															with jax.disable_jit():
																		UpperCamelCase__   =    model_jitted(**snake_case       ).to_tuple()

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



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

						'''simple docstring'''


						return AutoImageProcessor.from_pretrained("microsoft/resnet-50"       ) if is_vision_available() else None







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"       )

						UpperCamelCase__   =    self.default_image_processor
						UpperCamelCase__   =    prepare_img()
						UpperCamelCase__   =    image_processor(images=snake_case ,  return_tensors="np"       )

						UpperCamelCase__   =    model(**snake_case       )

						# verify the logits
						UpperCamelCase__   =    (1, 1000)
						self.assertEqual(outputs.logits.shape ,  snake_case       )

						UpperCamelCase__   =    jnp.array([-11.1069, -9.7877, -8.3777]       )

						self.assertTrue(jnp.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()
__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    '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     UpperCamelCase_(			_A				:int			,       _A				:Dict			,       _A				:int			,       _A				:List[str]			,       _A				:Optional[int]					)->						Any:
			for attribute in key.split("."					):
						UpperCamelCase__   =    getattr(_A			,       _A					)

			if weight_type is not None:
						UpperCamelCase__   =    getattr(_A			,       _A					).shape
			else:
						UpperCamelCase__   =    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__   =    value
			elif weight_type == "weight_g":
						UpperCamelCase__   =    value
			elif weight_type == "weight_v":
						UpperCamelCase__   =    value
			elif weight_type == "bias":
						UpperCamelCase__   =    value
			else:
						UpperCamelCase__   =    value

			logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''					)
def     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:Dict					)->						List[Any]:
			UpperCamelCase__   =    []
			UpperCamelCase__   =    fairseq_model.state_dict()

			UpperCamelCase__   =    hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCamelCase__   =    False
						if "conv_layers" in name:
									load_conv_layer(
									    _A			,       _A			,       _A			,       _A			,       hf_model.config.feat_extract_norm == "group"			,       )
									UpperCamelCase__   =    True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCamelCase__   =    mapped_key

												if key in name:
															UpperCamelCase__   =    True
															if "*" in mapped_key:
																		UpperCamelCase__   =    name.split(_A					)[0].split("."					)[-2]
																		UpperCamelCase__   =    mapped_key.replace("*"			,       _A					)
															if "weight_g" in name:
																		UpperCamelCase__   =    "weight_g"
															elif "weight_v" in name:
																		UpperCamelCase__   =    "weight_v"
															elif "weight" in name:
																		UpperCamelCase__   =    "weight"
															elif "bias" in name:
																		UpperCamelCase__   =    "bias"
															else:
																		UpperCamelCase__   =    None
															set_recursively(_A			,       _A			,       _A			,       _A			,       _A					)
												continue
						if not is_used:
									unused_weights.append(_A					)

			logger.warning(F'''Unused weights: {unused_weights}'''					)
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]			,       _A				:Union[str, Any]			,       _A				:Dict			,       _A				:Union[str, Any]					)->						Optional[Any]:
			UpperCamelCase__   =    full_name.split("conv_layers."					)[-1]
			UpperCamelCase__   =    name.split("."					)
			UpperCamelCase__   =    int(items[0]					)
			UpperCamelCase__   =    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__   =    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__   =    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__   =    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__   =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(_A					)
def     UpperCamelCase_(			_A				:Dict					)->						Union[str, Any]:
			UpperCamelCase__   =    HubertConfig()
			UpperCamelCase__   =    model.config

			UpperCamelCase__   =    fs_config.activation_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    fs_config.attention_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    eval(fs_config.extractor_conv_feature_layers					)
			UpperCamelCase__   =    [x[0] for x in conv_layers]
			UpperCamelCase__   =    [x[1] for x in conv_layers]
			UpperCamelCase__   =    [x[2] for x in conv_layers]
			UpperCamelCase__   =    "gelu"
			UpperCamelCase__   =    "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			UpperCamelCase__   =    False
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.activation_fn
			UpperCamelCase__   =    fs_config.dropout
			UpperCamelCase__   =    fs_config.encoder_embed_dim
			UpperCamelCase__   =    0.0_2
			UpperCamelCase__   =    fs_config.encoder_ffn_embed_dim
			UpperCamelCase__   =    1e-5
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.encoder_attention_heads
			UpperCamelCase__   =    fs_config.conv_pos_groups
			UpperCamelCase__   =    fs_config.conv_pos
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    fs_config.encoder_layers

			return config
@torch.no_grad()
def     UpperCamelCase_(			_A				:str			,       _A				:Union[str, Any]=None					)->						str:
			UpperCamelCase__   =    distilhubert().model.model

			if config_path is not None:
						UpperCamelCase__   =    HubertConfig.from_pretrained(_A					)
			else:
						UpperCamelCase__   =    convert_config(_A					)
			UpperCamelCase__   =    model.eval()

			UpperCamelCase__   =    WavaVecaFeatureExtractor(
			    feature_size=1			,       sampling_rate=1_60_00			,       padding_value=0			,       do_normalize=_A			,       return_attention_mask=_A			,       )
			UpperCamelCase__   =    HubertModel(_A					)

			recursively_load_weights(_A			,       _A					)

			feature_extractor.save_pretrained(_A					)
			hf_model.save_pretrained(_A					)


if __name__ == "__main__":
						__UpperCamelCase      						=    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')
						__UpperCamelCase      						=    parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


