
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_A   =     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			for attribute in key.split("."				):
						lowerCAmelCase_						=	getattr(__lowerCamelCase    ,							__lowerCamelCase				)

			if weight_type is not None:
						lowerCAmelCase_						=	getattr(__lowerCamelCase    ,							__lowerCamelCase				).shape
			else:
						lowerCAmelCase_						=	hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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
			elif weight_type == "running_mean":
						lowerCAmelCase_						=	value
			elif weight_type == "running_var":
						lowerCAmelCase_						=	value
			elif weight_type == "num_batches_tracked":
						lowerCAmelCase_						=	value
			elif weight_type == "inv_freq":
						lowerCAmelCase_						=	value
			else:
						lowerCAmelCase_						=	value

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	[]
			lowerCAmelCase_						=	fairseq_model.state_dict()

			lowerCAmelCase_						=	hf_model.wavaveca_conformer.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCAmelCase_						=	False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							hf_model.config.feat_extract_norm == "group"    ,							)
									lowerCAmelCase_						=	True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCAmelCase_						=	"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
															lowerCAmelCase_						=	True
															if "*" in mapped_key:
																		lowerCAmelCase_						=	name.split(__lowerCamelCase				)[0].split("."				)[-2]
																		lowerCAmelCase_						=	mapped_key.replace("*"    ,							__lowerCamelCase				)
															if "pos_bias_u" in name:
																		lowerCAmelCase_						=	None
															elif "pos_bias_v" in name:
																		lowerCAmelCase_						=	None
															elif "weight_g" in name:
																		lowerCAmelCase_						=	"weight_g"
															elif "weight_v" in name:
																		lowerCAmelCase_						=	"weight_v"
															elif "bias" in name:
																		lowerCAmelCase_						=	"bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCAmelCase_						=	"weight"
															elif "running_mean" in name:
																		lowerCAmelCase_						=	"running_mean"
															elif "inv_freq" in name:
																		lowerCAmelCase_						=	"inv_freq"
															elif "running_var" in name:
																		lowerCAmelCase_						=	"running_var"
															elif "num_batches_tracked" in name:
																		lowerCAmelCase_						=	"num_batches_tracked"
															else:
																		lowerCAmelCase_						=	None
															set_recursively(__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase				)
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase				)

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Dict				):




			"""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:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    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:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[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(__lowerCamelCase				)






@torch.no_grad()
def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  List[Any]=None    ,							__lowerCAmelCase  :  List[str]=None    ,							__lowerCAmelCase  :  Union[str, Any]=True				):




			"""simple docstring"""


			if config_path is not None:
						lowerCAmelCase_						=	WavaVecaConformerConfig.from_pretrained(__lowerCamelCase    ,							hidden_act="swish"				)
			else:
						lowerCAmelCase_						=	WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						lowerCAmelCase_						=	"rotary"

			if is_finetuned:
						if dict_path:
									lowerCAmelCase_						=	Dictionary.load(__lowerCamelCase				)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCAmelCase_						=	target_dict.pad_index
									lowerCAmelCase_						=	target_dict.bos_index
									lowerCAmelCase_						=	target_dict.eos_index
									lowerCAmelCase_						=	len(target_dict.symbols				)
									lowerCAmelCase_						=	os.path.join(__lowerCamelCase    ,							"vocab.json"				)
									if not os.path.isdir(__lowerCamelCase				):
												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCamelCase				)				)
												return
									os.makedirs(__lowerCamelCase    ,							exist_ok=__lowerCamelCase				)
									lowerCAmelCase_						=	target_dict.indices

									# fairseq has the <pad> and <s> switched
									lowerCAmelCase_						=	0
									lowerCAmelCase_						=	1
									with open(__lowerCamelCase    ,							"w"    ,							encoding="utf-8"				) as vocab_handle:
												json.dump(__lowerCamelCase    ,							__lowerCamelCase				)
									lowerCAmelCase_						=	WavaVecaCTCTokenizer(
									    __lowerCamelCase    ,							unk_token=target_dict.unk_word    ,							pad_token=target_dict.pad_word    ,							bos_token=target_dict.bos_word    ,							eos_token=target_dict.eos_word    ,							word_delimiter_token="|"    ,							do_lower_case=__lowerCamelCase    ,							)
									lowerCAmelCase_						=	True if config.feat_extract_norm == "layer" else False
									lowerCAmelCase_						=	WavaVecaFeatureExtractor(
									    feature_size=1    ,							sampling_rate=16000    ,							padding_value=0    ,							do_normalize=__lowerCamelCase    ,							return_attention_mask=__lowerCamelCase    ,							)
									lowerCAmelCase_						=	WavaVecaProcessor(feature_extractor=__lowerCamelCase    ,							tokenizer=__lowerCamelCase				)
									processor.save_pretrained(__lowerCamelCase				)

						lowerCAmelCase_						=	WavaVecaConformerForCTC(__lowerCamelCase				)
			else:
						lowerCAmelCase_						=	WavaVecaConformerForPreTraining(__lowerCamelCase				)

			if is_finetuned:
						lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]    ,							arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
			else:
						lowerCAmelCase_						=	argparse.Namespace(task="audio_pretraining"				)
						lowerCAmelCase_						=	fairseq.tasks.setup_task(__lowerCamelCase				)

						lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,							task=__lowerCamelCase				)

			lowerCAmelCase_						=	model[0].eval()

			recursively_load_weights(__lowerCamelCase    ,							__lowerCamelCase    ,							not is_finetuned				)

			hf_wavavec.save_pretrained(__lowerCamelCase				)


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("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
			parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument(
			    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
			)
			_A   =     parser.parse_args()
			convert_wavaveca_conformer_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class       lowerCAmelCase__				:







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







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Tuple			):
       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      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       TFResNetModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # 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      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.num_labels
       _snake_case					     =       TFResNetForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       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			:			List[Any]			):
       return







     @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			int			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			str			):
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       model(**self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)			)

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

         _snake_case					     =       self.model_tester.num_stages
         self.assertEqual(len(_lowerCamelCase			)    ,      expected_num_stages + 1			)

         # ResNet'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 // 4, self.model_tester.image_size // 4]    ,      )

       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       _snake_case					     =       ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
         for layer_type in layers_type:
           _snake_case					     =       layer_type
           _snake_case					     =       True
           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

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

           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFResNetModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

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







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

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



