





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.albert.modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				)







class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def __init__(						self    :  Tuple ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Union[str, Any]=13 ,					__lowercase    :  List[Any]=7 ,					__lowercase    :  Any=True ,					__lowercase    :  Optional[int]=True ,					__lowercase    :  str=True ,					__lowercase    :  Tuple=True ,					__lowercase    :  Optional[Any]=99 ,					__lowercase    :  int=32 ,					__lowercase    :  Tuple=5 ,					__lowercase    :  int=4 ,					__lowercase    :  Dict=37 ,					__lowercase    :  List[Any]="gelu" ,					__lowercase    :  Any=0.1 ,					__lowercase    :  int=0.1 ,					__lowercase    :  int=5_12 ,					__lowercase    :  Any=16 ,					__lowercase    :  List[Any]=2 ,					__lowercase    :  Tuple=0.02 ,					__lowercase    :  Tuple=4 ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     seq_length
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_attention_mask
						UpperCAmelCase_								=     use_token_type_ids
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     hidden_size
						UpperCAmelCase_								=     num_hidden_layers
						UpperCAmelCase_								=     num_attention_heads
						UpperCAmelCase_								=     intermediate_size
						UpperCAmelCase_								=     hidden_act
						UpperCAmelCase_								=     hidden_dropout_prob
						UpperCAmelCase_								=     attention_probs_dropout_prob
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     type_vocab_size
						UpperCAmelCase_								=     type_sequence_label_size
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     num_choices





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size		)

						UpperCAmelCase_								=     None
						if self.use_attention_mask:
							UpperCAmelCase_								=     random_attention_mask([self.batch_size, self.seq_length]		)

						UpperCAmelCase_								=     None
						if self.use_token_type_ids:
							UpperCAmelCase_								=     ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size		)

						UpperCAmelCase_								=     AlbertConfig(
						    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=__lowercase ,					initializer_range=self.initializer_range ,					)

						return config, input_ids, token_type_ids, attention_mask





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.prepare_config_and_inputs()
						UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     config_and_inputs
						UpperCAmelCase_								=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
						return config, inputs_dict







@require_flax
class   _UpperCamelCase  (       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     List[str]			    =       (
					    (
					        FlaxAlbertModel,
					        FlaxAlbertForPreTraining,
					        FlaxAlbertForMaskedLM,
					        FlaxAlbertForMultipleChoice,
					        FlaxAlbertForQuestionAnswering,
					        FlaxAlbertForSequenceClassification,
					        FlaxAlbertForTokenClassification,
					        FlaxAlbertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     FlaxAlbertModelTester(self		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						for model_class_name in self.all_model_classes:
							UpperCAmelCase_								=     model_class_name.from_pretrained("""albert-base-v2"""		)
							UpperCAmelCase_								=     model(np.ones((1, 1)		)		)
							self.assertIsNotNone(__lowercase		)







@require_flax
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     FlaxAlbertModel.from_pretrained("""albert-base-v2"""		)
						UpperCAmelCase_								=     np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]		)
						UpperCAmelCase_								=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
						UpperCAmelCase_								=     model(__lowercase ,					attention_mask=__lowercase		)[0]
						UpperCAmelCase_								=     (1, 11, 7_68)
						self.assertEqual(output.shape ,					__lowercase		)
						UpperCAmelCase_								=     np.array(
						    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

						self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,					__lowercase ,					atol=1e-4		)		)









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()
UpperCamelCase__   : str					  = logging.get_logger(__name__)

UpperCamelCase__   : 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    A_(			A				,      A				,      A				,      A				,      A   ):
	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    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     fairseq_model.state_dict()

	UpperCAmelCase_								=     hf_model.sew.feature_extractor if is_finetuned else 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_								=     """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]:
					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    A_(			A				,      A				,      A				,      A				,      A   ):
	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    A_(			A				,      A   ):
	UpperCAmelCase_								=     SEWConfig()
	if is_finetuned:
		UpperCAmelCase_								=     model.wav_encoder.wav_model.cfg
	else:
		UpperCAmelCase_								=     model.cfg

	UpperCAmelCase_								=     fs_config.conv_bias
	UpperCAmelCase_								=     eval(fs_config.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_								=     0.0
	UpperCAmelCase_								=     fs_config.activation_fn.name
	UpperCAmelCase_								=     fs_config.encoder_embed_dim
	UpperCAmelCase_								=     0.02
	UpperCAmelCase_								=     fs_config.encoder_ffn_embed_dim
	UpperCAmelCase_								=     1E-5
	UpperCAmelCase_								=     fs_config.encoder_layerdrop
	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
	UpperCAmelCase_								=     fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		UpperCAmelCase_								=     model.cfg
		UpperCAmelCase_								=     fs_config.final_dropout
		UpperCAmelCase_								=     fs_config.layerdrop
	UpperCAmelCase_								=     fs_config.activation_dropout
	UpperCAmelCase_								=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	UpperCAmelCase_								=     fs_config.attention_dropout
	UpperCAmelCase_								=     fs_config.dropout_input
	UpperCAmelCase_								=     fs_config.dropout
	UpperCAmelCase_								=     fs_config.mask_channel_length
	UpperCAmelCase_								=     fs_config.mask_channel_prob
	UpperCAmelCase_								=     fs_config.mask_length
	UpperCAmelCase_								=     fs_config.mask_prob

	UpperCAmelCase_								=     """Wav2Vec2FeatureExtractor"""
	UpperCAmelCase_								=     """Wav2Vec2CTCTokenizer"""

	return config




@torch.no_grad()
def    A_(			A				,      A				,      A=None				,      A=None				,      A=True   ):

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

	if config_path is not None:
		UpperCAmelCase_								=     SEWConfig.from_pretrained(A   )
	else:
		UpperCAmelCase_								=     convert_config(model[0]				,      A   )
	UpperCAmelCase_								=     model[0].eval()

	UpperCAmelCase_								=     True if config.feat_extract_norm == """layer""" else False
	UpperCAmelCase_								=     WavaVecaFeatureExtractor(
	    feature_size=1				,      sampling_rate=16000				,      padding_value=0				,      do_normalize=A				,      return_attention_mask=A				,      )

	if is_finetuned:
		if dict_path:
			UpperCAmelCase_								=     Dictionary.load(A   )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			UpperCAmelCase_								=     target_dict.pad_index
			UpperCAmelCase_								=     target_dict.bos_index
			UpperCAmelCase_								=     target_dict.pad_index
			UpperCAmelCase_								=     target_dict.bos_index
			UpperCAmelCase_								=     target_dict.eos_index
			UpperCAmelCase_								=     len(target_dict.symbols   )
			UpperCAmelCase_								=     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   )
			UpperCAmelCase_								=     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				,      )
			UpperCAmelCase_								=     WavaVecaProcessor(feature_extractor=A				,      tokenizer=A   )
			processor.save_pretrained(A   )

		UpperCAmelCase_								=     SEWForCTC(A   )
	else:
		UpperCAmelCase_								=     SEWModel(A   )
		feature_extractor.save_pretrained(A   )

	recursively_load_weights(A				,      A				,      A   )

	hf_model.save_pretrained(A   )


if __name__ == "__main__":
				UpperCamelCase__   : str					  = 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"""
				)
				UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)


