
"""simple docstring"""

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()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    					=						{
    '''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''',
}
SCREAMING_SNAKE_CASE_    					=						[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Optional[Any] ) ->      Any:






    """simple docstring"""




    for attribute in key.split('.' ):
        UpperCAmelCase									=					getattr(_lowerCamelCase ,				_lowerCamelCase )

    if weight_type is not None:
        UpperCAmelCase									=					getattr(_lowerCamelCase ,				_lowerCamelCase ).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     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Any ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					[]
    UpperCAmelCase									=					fairseq_model.state_dict()

    UpperCAmelCase									=					hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase									=					False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase ,				hf_model.config.feat_extract_norm == 'group' ,				)
            UpperCAmelCase									=					True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
                    UpperCAmelCase									=					True
                    if "*" in mapped_key:
                        UpperCAmelCase									=					name.split(_lowerCamelCase )[0].split('.' )[-2]
                        UpperCAmelCase									=					mapped_key.replace('*' ,				_lowerCamelCase )
                    if "weight_g" in name:
                        UpperCAmelCase									=					"""weight_g"""
                    elif "weight_v" in name:
                        UpperCAmelCase									=					"""weight_v"""
                    elif "bias" in name and "relative_attention_bias" not in name:
                        UpperCAmelCase									=					"""bias"""
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCAmelCase									=					"""weight"""
                    else:
                        UpperCAmelCase									=					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     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Optional[Any] ) ->      Tuple:






    """simple docstring"""




    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(_lowerCamelCase )




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Optional[Any]=None ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					torch.load(_lowerCamelCase )
    UpperCAmelCase									=					WavLMConfigOrig(checkpoint['cfg'] )
    UpperCAmelCase									=					WavLMOrig(_lowerCamelCase )
    model.load_state_dict(checkpoint['model'] )
    model.eval()

    if config_path is not None:
        UpperCAmelCase									=					WavLMConfig.from_pretrained(_lowerCamelCase )
    else:
        UpperCAmelCase									=					WavLMConfig()

    UpperCAmelCase									=					WavLMModel(_lowerCamelCase )

    recursively_load_weights(_lowerCamelCase ,				_lowerCamelCase )

    hf_wavlm.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						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''')
   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					image_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					embeddings_size
          UpperCAmelCase									=					hidden_sizes
          UpperCAmelCase									=					depths
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					num_labels
          UpperCAmelCase									=					scope
          UpperCAmelCase									=					len(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase									=					self.get_config()

          return config, pixel_values




      def     a_      (							self							)						->					Optional[Any]:
          return RegNetConfig(
              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     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.num_labels
          UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	False




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxRegNetModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          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     a_      (							self							)						->					Optional[Any]:
          return




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      @unittest.skip(reason='RegNet does not use inputs_embeds'							)
      def     a_      (							self							)						->					List[str]:
          pass




      @unittest.skip(reason='RegNet does not support input and output embeddings'							)
      def     a_      (							self							)						->					Optional[int]:
          pass




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase									=					['pixel_values']
              self.assertListEqual(arg_names[:1]   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					model_class(lowercase_							)

              UpperCAmelCase									=					model(**self._prepare_for_class(lowercase_   ,       lowercase_							)							)

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

              UpperCAmelCase									=					self.model_tester.num_stages
              self.assertEqual(len(lowercase_							)   ,       expected_num_stages + 1							)

          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					True
              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)

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

              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def model_jitted(lowercase_   ,       **lowercase_							):
                      return model(pixel_values=lowercase_   ,       **lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




def     lowercase__						(					) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @cached_property
      def     a_      (							self							)						->					List[str]:
          return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'							) if is_vision_available() else None




      @slow
      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

          UpperCAmelCase									=					self.default_image_processor
          UpperCAmelCase									=					prepare_img()
          UpperCAmelCase									=					image_processor(images=lowercase_   ,       return_tensors='np'							)

          UpperCAmelCase									=					model(**lowercase_							)

          # verify the logits
          UpperCAmelCase									=					(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape   ,       lowercase_							)

          UpperCAmelCase									=					jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

          self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,       lowercase_   ,       atol=1E-4							)							)



