

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase     : Optional[int]       =						logging.get_logger(__name__)

__lowercase     : List[Any]       =						{
    '''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}







def       lowercase					(	__A :					Optional[Any] ,    __A :					List[str] ,    __A :					Union[str, Any] ,    __A :					Optional[Any] ,    __A :					str		)				->			Union[str, Any]:





    '''simple docstring'''




    for attribute in key.split("""."""		):
        snake_case							:		Optional[int]        =		getattr(__A ,    __A		)

    if weight_type is not None:
        snake_case							:		Tuple        =		getattr(__A ,    __A		).shape
    else:
        snake_case							:		Dict        =		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":
        snake_case							:		Optional[int]        =		value
    elif weight_type == "weight_g":
        snake_case							:		int        =		value
    elif weight_type == "weight_v":
        snake_case							:		Union[str, Any]        =		value
    elif weight_type == "bias":
        snake_case							:		int        =		value
    else:
        snake_case							:		str        =		value

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







def       lowercase					(	__A :					Tuple ,    __A :					Optional[int] ,    __A :					int		)				->			int:





    '''simple docstring'''




    snake_case							:		Any        =		[]
    snake_case							:		Optional[int]        =		fairseq_model.state_dict()

    snake_case							:		Tuple        =		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case							:		List[Any]        =		False
        if "conv_layers" in name:
            load_conv_layer(
                __A ,    __A ,    __A ,    __A ,    hf_model.config.feat_extract_norm == """group""" ,    )
            snake_case							:		Dict        =		True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case							:		Dict        =		"""sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                    snake_case							:		str        =		True
                    if "*" in mapped_key:
                        snake_case							:		Optional[int]        =		name.split(__A		)[0].split("""."""		)[-2]
                        snake_case							:		Optional[Any]        =		mapped_key.replace("""*""" ,    __A		)
                    if "weight_g" in name:
                        snake_case							:		Dict        =		"""weight_g"""
                    elif "weight_v" in name:
                        snake_case							:		List[Any]        =		"""weight_v"""
                    elif "weight" in name:
                        snake_case							:		Tuple        =		"""weight"""
                    elif "bias" in name:
                        snake_case							:		int        =		"""bias"""
                    else:
                        snake_case							:		List[Any]        =		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       lowercase					(	__A :					List[Any] ,    __A :					Any ,    __A :					Optional[Any] ,    __A :					Optional[Any] ,    __A :					Optional[int]		)				->			Any:





    '''simple docstring'''




    snake_case							:		str        =		full_name.split("""conv_layers."""		)[-1]
    snake_case							:		List[Any]        =		name.split("""."""		)
    snake_case							:		Tuple        =		int(items[0]		)
    snake_case							:		Any        =		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."""
            )
            snake_case							:		str        =		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."""
            )
            snake_case							:		Any        =		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."
            )
            snake_case							:		Optional[int]        =		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."""
            )
            snake_case							:		Optional[int]        =		value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
    else:
        unused_weights.append(__A		)







def       lowercase					(	__A :					Optional[Any] ,    __A :					List[str]		)				->			str:





    '''simple docstring'''




    snake_case							:		Tuple        =		SEWConfig()
    if is_finetuned:
        snake_case							:		Dict        =		model.wav_encoder.wav_model.cfg
    else:
        snake_case							:		str        =		model.cfg

    snake_case							:		Union[str, Any]        =		fs_config.conv_bias
    snake_case							:		Any        =		eval(fs_config.conv_feature_layers		)
    snake_case							:		Any        =		[x[0] for x in conv_layers]
    snake_case							:		Dict        =		[x[1] for x in conv_layers]
    snake_case							:		Optional[int]        =		[x[2] for x in conv_layers]
    snake_case							:		str        =		"""gelu"""
    snake_case							:		Tuple        =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
    snake_case							:		List[Any]        =		0.0
    snake_case							:		int        =		fs_config.activation_fn.name
    snake_case							:		Any        =		fs_config.encoder_embed_dim
    snake_case							:		Optional[int]        =		0.02
    snake_case							:		Dict        =		fs_config.encoder_ffn_embed_dim
    snake_case							:		int        =		1E-5
    snake_case							:		List[Any]        =		fs_config.encoder_layerdrop
    snake_case							:		List[Any]        =		fs_config.encoder_attention_heads
    snake_case							:		Dict        =		fs_config.conv_pos_groups
    snake_case							:		Dict        =		fs_config.conv_pos
    snake_case							:		Union[str, Any]        =		len(__A		)
    snake_case							:		Dict        =		fs_config.encoder_layers
    snake_case							:		Tuple        =		fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        snake_case							:		List[Any]        =		model.cfg
        snake_case							:		Optional[int]        =		fs_config.final_dropout
        snake_case							:		List[str]        =		fs_config.layerdrop
    snake_case							:		int        =		fs_config.activation_dropout
    snake_case							:		List[Any]        =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    snake_case							:		Union[str, Any]        =		fs_config.attention_dropout
    snake_case							:		List[Any]        =		fs_config.dropout_input
    snake_case							:		List[Any]        =		fs_config.dropout
    snake_case							:		int        =		fs_config.mask_channel_length
    snake_case							:		Any        =		fs_config.mask_channel_prob
    snake_case							:		Dict        =		fs_config.mask_length
    snake_case							:		Dict        =		fs_config.mask_prob

    snake_case							:		Optional[Any]        =		"""Wav2Vec2FeatureExtractor"""
    snake_case							:		Tuple        =		"""Wav2Vec2CTCTokenizer"""

    return config







@torch.no_grad()
def       lowercase					(	__A :					Any ,    __A :					str ,    __A :					Any=None ,    __A :					int=None ,    __A :					Union[str, Any]=True		)				->			List[str]:





    '''simple docstring'''




    if is_finetuned:
        snake_case      ,		snake_case      ,		snake_case							:		List[str]        =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path] ,    arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
    else:
        snake_case      ,		snake_case      ,		snake_case							:		str        =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

    if config_path is not None:
        snake_case							:		int        =		SEWConfig.from_pretrained(__A		)
    else:
        snake_case							:		List[str]        =		convert_config(model[0] ,    __A		)
    snake_case							:		Optional[Any]        =		model[0].eval()

    snake_case							:		Union[str, Any]        =		True if config.feat_extract_norm == """layer""" else False
    snake_case							:		List[Any]        =		WavaVecaFeatureExtractor(
        feature_size=1 ,    sampling_rate=1_6000 ,    padding_value=0 ,    do_normalize=__A ,    return_attention_mask=__A ,    )

    if is_finetuned:
        if dict_path:
            snake_case							:		str        =		Dictionary.load(__A		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            snake_case							:		Optional[Any]        =		target_dict.pad_index
            snake_case							:		List[Any]        =		target_dict.bos_index
            snake_case							:		Union[str, Any]        =		target_dict.pad_index
            snake_case							:		Tuple        =		target_dict.bos_index
            snake_case							:		Union[str, Any]        =		target_dict.eos_index
            snake_case							:		str        =		len(target_dict.symbols		)
            snake_case							:		Tuple        =		os.path.join(__A ,    """vocab.json"""		)
            if not os.path.isdir(__A		):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__A		)		)
                return
            os.makedirs(__A ,    exist_ok=__A		)
            with open(__A ,    """w""" ,    encoding="""utf-8"""		) as vocab_handle:
                json.dump(target_dict.indices ,    __A		)
            snake_case							:		int        =		WavaVecaCTCTokenizer(
                __A ,    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=__A ,    )
            snake_case							:		Tuple        =		WavaVecaProcessor(feature_extractor=__A ,    tokenizer=__A		)
            processor.save_pretrained(__A		)

        snake_case							:		Any        =		SEWForCTC(__A		)
    else:
        snake_case							:		Tuple        =		SEWModel(__A		)
        feature_extractor.save_pretrained(__A		)

    recursively_load_weights(__A ,    __A ,    __A		)

    hf_model.save_pretrained(__A		)


if __name__ == "__main__":
     __lowercase     : 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('''--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(
         '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     __lowercase     : Dict       =						parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )





"""simple docstring"""




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:


  class       __magic_name__							:
         @staticmethod
         def 				lowercase_				(  *A_			,					**A_							)  ->						Optional[int]:





           """simple docstring"""




           pass


@is_pipeline_test
@require_vision
class       __magic_name__							(							unittest.TestCase			):
       @require_torch
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  pipeline(
             model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,					)
         _lowercase:      List[Any]   			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
         _lowercase:      str   			=  image_classifier(A_			,					candidate_labels=['''a''', '''b''', '''c''']							)

         # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
         # python and torch versions.
         self.assertIn(
             nested_simplify(A_							)			,					[
                 [{'''score''': 0.3_33, '''label''': '''a'''}, {'''score''': 0.3_33, '''label''': '''b'''}, {'''score''': 0.3_33, '''label''': '''c'''}],
                 [{'''score''': 0.3_33, '''label''': '''a'''}, {'''score''': 0.3_33, '''label''': '''c'''}, {'''score''': 0.3_33, '''label''': '''b'''}],
             ]			,					)

         _lowercase:      Any   			=  image_classifier([image] * 5			,					candidate_labels=['''A''', '''B''', '''C''']			,					batch_size=2							)
         self.assertEqual(
             nested_simplify(A_							)			,					[
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
             ]			,					)
       @require_tf
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  pipeline(
             model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,					framework='''tf'''							)
         _lowercase:      List[str]   			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
         _lowercase:      Union[str, Any]   			=  image_classifier(A_			,					candidate_labels=['''a''', '''b''', '''c''']							)

         self.assertEqual(
             nested_simplify(A_							)			,					[{'''score''': 0.3_33, '''label''': '''a'''}, {'''score''': 0.3_33, '''label''': '''b'''}, {'''score''': 0.3_33, '''label''': '''c'''}]			,					)

         _lowercase:      List[str]   			=  image_classifier([image] * 5			,					candidate_labels=['''A''', '''B''', '''C''']			,					batch_size=2							)
         self.assertEqual(
             nested_simplify(A_							)			,					[
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
                 [
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                     {'''score''': 0.3_33, '''label''': ANY(A_							)},
                 ],
             ]			,					)
       @slow
       @require_torch
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Tuple   			=  pipeline(
             task='''zero-shot-image-classification'''			,					model='''openai/clip-vit-base-patch32'''			,					)
         # This is an image of 2 cats with remotes and no planes
         _lowercase:      str   			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
         _lowercase:      Optional[Any]   			=  image_classifier(A_			,					candidate_labels=['''cat''', '''plane''', '''remote''']							)

         self.assertEqual(
             nested_simplify(A_							)			,					[
                 {'''score''': 0.5_11, '''label''': '''remote'''},
                 {'''score''': 0.4_85, '''label''': '''cat'''},
                 {'''score''': 0.0_04, '''label''': '''plane'''},
             ]			,					)

         _lowercase:      Optional[Any]   			=  image_classifier([image] * 5			,					candidate_labels=['''cat''', '''plane''', '''remote''']			,					batch_size=2							)
         self.assertEqual(
             nested_simplify(A_							)			,					[
                 [
                     {'''score''': 0.5_11, '''label''': '''remote'''},
                     {'''score''': 0.4_85, '''label''': '''cat'''},
                     {'''score''': 0.0_04, '''label''': '''plane'''},
                 ],
             ]
             * 5			,					)



       @slow
       @require_tf
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  pipeline(
             task='''zero-shot-image-classification'''			,					model='''openai/clip-vit-base-patch32'''			,					framework='''tf'''							)
         # This is an image of 2 cats with remotes and no planes
         _lowercase:      str   			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
         _lowercase:      Optional[int]   			=  image_classifier(A_			,					candidate_labels=['''cat''', '''plane''', '''remote''']							)
         self.assertEqual(
             nested_simplify(A_							)			,					[
                 {'''score''': 0.5_11, '''label''': '''remote'''},
                 {'''score''': 0.4_85, '''label''': '''cat'''},
                 {'''score''': 0.0_04, '''label''': '''plane'''},
             ]			,					)

         _lowercase:      Optional[Any]   			=  image_classifier([image] * 5			,					candidate_labels=['''cat''', '''plane''', '''remote''']			,					batch_size=2							)
         self.assertEqual(
             nested_simplify(A_							)			,					[
                 [
                     {'''score''': 0.5_11, '''label''': '''remote'''},
                     {'''score''': 0.4_85, '''label''': '''cat'''},
                     {'''score''': 0.0_04, '''label''': '''plane'''},
                 ],
             ]
             * 5			,					)
