



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

A__     				=	{
    """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    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					for attribute in key.split("""."""      ):
										_lowerCAmelCase      =   getattr(snake_case     ,						snake_case      )

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

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   []
					_lowerCAmelCase      =   fairseq_model.state_dict()

					_lowerCAmelCase      =   hf_model.feature_extractor

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

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

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   full_name.split("""conv_layers."""      )[-1]
					_lowerCAmelCase      =   name.split("""."""      )
					_lowerCAmelCase      =   int(items[0]      )
					_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   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."
															)
															_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
					else:
										unused_weights.append(snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   HubertConfig()
					_lowerCAmelCase      =   model.config

					_lowerCAmelCase      =   fs_config.activation_dropout
					_lowerCAmelCase      =   False
					_lowerCAmelCase      =   fs_config.attention_dropout
					_lowerCAmelCase      =   False
					_lowerCAmelCase      =   eval(fs_config.extractor_conv_feature_layers      )
					_lowerCAmelCase      =   [x[0] for x in conv_layers]
					_lowerCAmelCase      =   [x[1] for x in conv_layers]
					_lowerCAmelCase      =   [x[2] for x in conv_layers]
					_lowerCAmelCase      =   """gelu"""
					_lowerCAmelCase      =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					_lowerCAmelCase      =   False
					_lowerCAmelCase      =   0.0
					_lowerCAmelCase      =   0.0
					_lowerCAmelCase      =   fs_config.activation_fn
					_lowerCAmelCase      =   fs_config.dropout
					_lowerCAmelCase      =   fs_config.encoder_embed_dim
					_lowerCAmelCase      =   0.02
					_lowerCAmelCase      =   fs_config.encoder_ffn_embed_dim
					_lowerCAmelCase      =   1E-5
					_lowerCAmelCase      =   0.0
					_lowerCAmelCase      =   fs_config.encoder_attention_heads
					_lowerCAmelCase      =   fs_config.conv_pos_groups
					_lowerCAmelCase      =   fs_config.conv_pos
					_lowerCAmelCase      =   len(snake_case      )
					_lowerCAmelCase      =   fs_config.encoder_layers

					return config


@torch.no_grad()
def 		_UpperCAmelCase    (				snake_case     ,						snake_case=None      ):





					"""simple docstring"""

					_lowerCAmelCase      =   distilhubert().model.model

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

					_lowerCAmelCase      =   WavaVecaFeatureExtractor(
					    feature_size=1     ,						sampling_rate=1_60_00     ,						padding_value=0     ,						do_normalize=snake_case     ,						return_attention_mask=snake_case     ,						)
					_lowerCAmelCase      =   HubertModel(snake_case      )

					recursively_load_weights(snake_case     ,						snake_case      )

					feature_extractor.save_pretrained(snake_case      )
					hf_model.save_pretrained(snake_case      )


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








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image





class  __lowerCAmelCase			(					unittest.TestCase	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   load_tool("""image-question-answering"""		)
									self.tool.setup()
									_lowerCAmelCase      =   load_tool("""image-question-answering"""						,    remote=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(_snake_case						,    """How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
									_lowerCAmelCase      =   self.remote_tool(image=_snake_case						,    question="""How many cats are sleeping on the couch?"""		)
									self.assertEqual(_snake_case						,    """2"""		)



