
import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, 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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_A   =			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__		)							->							List[str]:
    """simple docstring"""

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

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

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



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= []
    snake_case								= fairseq_model.state_dict()

    snake_case								= hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case								= False
        if "conv_layers" in name:
            load_conv_layer(
                A__			,		A__			,		A__			,		A__			,		hf_model.config.feat_extract_norm == "group"			,		)
            snake_case								= True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
                    snake_case								= True
                    if "*" in mapped_key:
                        snake_case								= name.split(A__		)[0].split("."		)[-2]
                        snake_case								= mapped_key.replace("*"			,		A__		)
                    if "weight_g" in name:
                        snake_case								= "weight_g"
                    elif "weight_v" in name:
                        snake_case								= "weight_v"
                    elif "bias" in name and "relative_attention_bias" not in name:
                        snake_case								= "bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case								= "weight"
                    else:
                        snake_case								= 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__			,		A__			,		A__			,		A__			,		A__		)							->							int:
    """simple docstring"""

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



@torch.no_grad()
def 						lowercase_      (       A__			,		A__			,		A__=None		)							->							int:
    """simple docstring"""

    snake_case								= torch.load(A__		)
    snake_case								= WavLMConfigOrig(checkpoint["cfg"]		)
    snake_case								= WavLMOrig(A__		)
    model.load_state_dict(checkpoint["model"]		)
    model.eval()

    if config_path is not None:
        snake_case								= WavLMConfig.from_pretrained(A__		)
    else:
        snake_case								= WavLMConfig()

    snake_case								= WavLMModel(A__		)

    recursively_load_weights(A__			,		A__		)

    hf_wavlm.save_pretrained(A__		)


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
   _A   =			parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       LiltForQuestionAnswering,
       LiltForSequenceClassification,
       LiltForTokenClassification,
       LiltModel,
   )
   from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class 					lowerCamelCase		:





     def __init__(self				:   Union[str, Any]    ,   _A				:   Any    ,   _A				:   Tuple=1_3    ,   _A				:   Optional[int]=7    ,   _A				:   Any=True    ,   _A				:   str=True    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[int]=True    ,   _A				:   str=9_9    ,   _A				:   str=2_4    ,   _A				:   int=2    ,   _A				:   Optional[Any]=6    ,   _A				:   int=3_7    ,   _A				:   List[Any]="gelu"    ,   _A				:   str=0.1    ,   _A				:   Dict=0.1    ,   _A				:   Dict=5_1_2    ,   _A				:   Tuple=1_6    ,   _A				:   List[str]=2    ,   _A				:   Dict=0.02    ,   _A				:   List[str]=3    ,   _A				:   Optional[Any]=None    ,   _A				:   Dict=1_0_0_0    ,   )						->						Any:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= seq_length
         snake_case								= is_training
         snake_case								= use_input_mask
         snake_case								= use_token_type_ids
         snake_case								= use_labels
         snake_case								= vocab_size
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= max_position_embeddings
         snake_case								= type_vocab_size
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range
         snake_case								= num_labels
         snake_case								= scope
         snake_case								= range_bbox





     def 		UpperCAmelCase(self				:   List[str]       )						->						List[str]:
         snake_case								= ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size       )

         snake_case								= ids_tensor([self.batch_size, self.seq_length, 4]    ,   self.range_bbox       )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]       ):
             for j in range(bbox.shape[1]       ):
                 if bbox[i, j, 3] < bbox[i, j, 1]:
                     snake_case								= bbox[i, j, 3]
                     snake_case								= bbox[i, j, 1]
                     snake_case								= t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     snake_case								= bbox[i, j, 2]
                     snake_case								= bbox[i, j, 0]
                     snake_case								= t

         snake_case								= None
         if self.use_input_mask:
             snake_case								= ids_tensor([self.batch_size, self.seq_length]    ,   vocab_size=2       )

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

         snake_case								= None
         snake_case								= None
         if self.use_labels:
             snake_case								= ids_tensor([self.batch_size]    ,   self.type_sequence_label_size       )
             snake_case								= ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels       )

         snake_case								= self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels





     def 		UpperCAmelCase(self				:   Tuple       )						->						Tuple:
         return LiltConfig(
             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    ,   initializer_range=self.initializer_range    ,   )





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Dict    ,   _A				:   List[Any]    ,   _A				:   Optional[Any]    ,   _A				:   Dict    ,   _A				:   str    ,   _A				:   Optional[Any]    ,   _A				:   Tuple    ,   )						->						Dict:
         snake_case								= LiltModel(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A    ,   token_type_ids=_A       )
         snake_case								= model(_A    ,   bbox=_A       )

         self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape    ,   (self.batch_size, self.hidden_size)       )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   Optional[int]    ,   _A				:   Dict    ,   _A				:   List[Any]    ,   _A				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   Tuple    ,   _A				:   Union[str, Any]    ,   )						->						Optional[int]:
         snake_case								= self.num_labels
         snake_case								= LiltForTokenClassification(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(
             _A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A    ,   labels=_A       )
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Any    ,   _A				:   List[str]    ,   _A				:   List[str]    ,   _A				:   Optional[int]    ,   _A				:   Optional[Any]    ,   )						->						Optional[int]:
         snake_case								= LiltForQuestionAnswering(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(
             _A    ,   bbox=_A    ,   attention_mask=_A    ,   token_type_ids=_A    ,   start_positions=_A    ,   end_positions=_A    ,   )
         self.parent.assertEqual(result.start_logits.shape    ,   (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape    ,   (self.batch_size, self.seq_length)       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							(
             snake_case
         )					,							
         )								= config_and_inputs
         snake_case								= {
             "input_ids": input_ids,
             "bbox": bbox,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict







@require_torch
class 					lowerCamelCase		(  A_							,			A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Optional[int]									=		(
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__    :							List[Any]									=		(
         {
             "feature-extraction": LiltModel,
             "question-answering": LiltForQuestionAnswering,
             "text-classification": LiltForSequenceClassification,
             "token-classification": LiltForTokenClassification,
             "zero-shot": LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__    :							Optional[Any]									=		False
     UpperCAmelCase__    :							Optional[int]									=		False





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   Dict    ,   _A				:   Union[str, Any]    ,   _A				:   int    ,   _A				:   Union[str, Any]       )						->						int:
         return True





     def 		UpperCAmelCase(self				:   str       )						->						Tuple:
         snake_case								= LiltModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						List[str]:
         self.config_tester.run_common_tests()





     def 		UpperCAmelCase(self				:   Tuple       )						->						Dict:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   int       )						->						Union[str, Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case								= type
             self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[Any]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Optional[int]:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_A       )





     @slow
     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Optional[Any]:
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= LiltModel.from_pretrained(_A       )
             self.assertIsNotNone(_A       )







@require_torch
@slow
class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         snake_case								= LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"       ).to(_A       )

         snake_case								= torch.tensor([[1, 2]]    ,   device=_A       )
         snake_case								= torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]    ,   device=_A       )

         # forward pass
         with torch.no_grad():
             snake_case								= model(input_ids=_A    ,   bbox=_A       )

         snake_case								= torch.Size([1, 2, 7_6_8]       )
         snake_case								= torch.tensor(
             [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]    ,   device=_A    ,   )

         self.assertTrue(outputs.last_hidden_state.shape    ,   _A       )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]    ,   _A    ,   atol=1E-3       )       )

