



'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetaImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=7     ,UpperCAmelCase_=3     ,UpperCAmelCase_=30     ,UpperCAmelCase_=400     ,UpperCAmelCase_=True     ,UpperCAmelCase_=None     ,UpperCAmelCase_=True     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=True     ,UpperCAmelCase_=1 / 255     ,UpperCAmelCase_=True     ,)  ->							Dict:
           lowercase__		       =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
           lowercase__		       =						parent
           lowercase__		       =						batch_size
           lowercase__		       =						num_channels
           lowercase__		       =						min_resolution
           lowercase__		       =						max_resolution
           lowercase__		       =						do_resize
           lowercase__		       =						size
           lowercase__		       =						do_normalize
           lowercase__		       =						image_mean
           lowercase__		       =						image_std
           lowercase__		       =						do_rescale
           lowercase__		       =						rescale_factor
           lowercase__		       =						do_pad



       def 							_a      (				self      )  ->							Optional[int]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False      )  ->							Optional[Any]:
           if not batched:
               lowercase__		       =						image_inputs[0]
               if isinstance(A_     ,Image.Image      ):
                   lowercase__       ,       lowercase__		       =						image.size
               else:
                   lowercase__       ,       lowercase__		       =						image.shape[1], image.shape[2]
               if w < h:
                   lowercase__		       =						int(self.size["shortest_edge"] * h / w      )
                   lowercase__		       =						self.size["shortest_edge"]
               elif w > h:
                   lowercase__		       =						self.size["shortest_edge"]
                   lowercase__		       =						int(self.size["shortest_edge"] * w / h      )
               else:
                   lowercase__		       =						self.size["shortest_edge"]
                   lowercase__		       =						self.size["shortest_edge"]

           else:
               lowercase__		       =						[]
               for image in image_inputs:
                   lowercase__       ,       lowercase__		       =						self.get_expected_values([image]      )
                   expected_values.append((expected_height, expected_width)      )
               lowercase__		       =						max(A_     ,key=lambda UpperCAmelCase_      : item[0]      )[0]
               lowercase__		       =						max(A_     ,key=lambda UpperCAmelCase_      : item[1]      )[1]

           return expected_height, expected_width





@require_torch
@require_vision
class 				snake_case (_SCREAMING_SNAKE_CASE		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[str]						   =					DetaImageProcessor if is_vision_available() else None



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						DetaImageProcessingTester(self      )



       @property
       def 							_a      (				self      )  ->							Optional[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(A_     ,"image_mean"      )      )
           self.assertTrue(hasattr(A_     ,"image_std"      )      )
           self.assertTrue(hasattr(A_     ,"do_normalize"      )      )
           self.assertTrue(hasattr(A_     ,"do_resize"      )      )
           self.assertTrue(hasattr(A_     ,"do_rescale"      )      )
           self.assertTrue(hasattr(A_     ,"do_pad"      )      )
           self.assertTrue(hasattr(A_     ,"size"      )      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size     ,{"shortest_edge": 18, "longest_edge": 1_333}      )
           self.assertEqual(image_processor.do_pad     ,A_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           pass



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           lowercase__		       =						prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_      )
           for image in image_inputs:
               self.assertIsInstance(A_     ,Image.Image      )

           # Test not batched input
           lowercase__		       =						image_processing(image_inputs[0]     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_      )

           self.assertEqual(
               encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

           # Test batched
           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_     ,batched=A_      )

           lowercase__		       =						image_processing(A_     ,return_tensors="pt"      ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )     ,)



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowercase__		       =						prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_     ,numpify=A_      )
           for image in image_inputs:
               self.assertIsInstance(A_     ,np.ndarray      )

           # Test not batched input
           lowercase__		       =						image_processing(image_inputs[0]     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_      )

           self.assertEqual(
               encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

           # Test batched
           lowercase__		       =						image_processing(A_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_     ,batched=A_      )

           self.assertEqual(
               encoded_images.shape     ,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )     ,)



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase__		       =						prepare_image_inputs(self.image_processor_tester     ,equal_resolution=A_     ,torchify=A_      )
           for image in image_inputs:
               self.assertIsInstance(A_     ,torch.Tensor      )

           # Test not batched input
           lowercase__		       =						image_processing(image_inputs[0]     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_      )

           self.assertEqual(
               encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

           # Test batched
           lowercase__		       =						image_processing(A_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(A_     ,batched=A_      )

           self.assertEqual(
               encoded_images.shape     ,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )     ,)



       @slow
       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
           with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,"r"      ) as f:
               lowercase__		       =						json.loads(f.read()      )

           lowercase__		       =						{"image_id": 39_769, "annotations": target}

           # encode them
           lowercase__		       =						DetaImageProcessor()
           lowercase__		       =						image_processing(images=A_     ,annotations=A_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,A_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,A_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([58_87.96_00, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,A_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,A_      )
           lowercase__		       =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,A_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,A_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,A_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([75, 75, 63, 65, 17, 17]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,A_      )      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,A_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,A_      )      )







       @slow
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
           with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,"r"      ) as f:
               lowercase__		       =						json.loads(f.read()      )

           lowercase__		       =						{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

           lowercase__		       =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

           # encode them
           lowercase__		       =						DetaImageProcessor(format="coco_panoptic"      )
           lowercase__		       =						image_processing(images=A_     ,annotations=A_     ,masks_path=A_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,A_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,A_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 58_79.65_62, 76_34.11_47]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,A_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,A_      )
           lowercase__		       =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,A_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,A_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,A_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([17, 17, 63, 75, 75, 93]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,A_      )      )
           # verify masks
           lowercase__		       =						822_873
           self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,A_      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,A_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,A_      )      )









'''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        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any     ,_snake_case  :						Optional[int]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

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

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




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        lowercase__		       =						False
        if "conv_layers" in name:
            load_conv_layer(
                _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,hf_model.config.feat_extract_norm == "group"     ,)
            lowercase__		       =						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                    lowercase__		       =						True
                    if "*" in mapped_key:
                        lowercase__		       =						name.split(_snake_case       )[0].split("."       )[-2]
                        lowercase__		       =						mapped_key.replace("*"     ,_snake_case       )
                    if "weight_g" in name:
                        lowercase__		       =						"weight_g"
                    elif "weight_v" in name:
                        lowercase__		       =						"weight_v"
                    elif "bias" in name and "relative_attention_bias" not in name:
                        lowercase__		       =						"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowercase__		       =						"weight"
                    else:
                        lowercase__		       =						None

                    set_recursively(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                continue
        if not is_used:
            unused_weights.append(_snake_case       )

    logger.warning(f'''Unused weights: {unused_weights}'''       )




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						int     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


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




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Any     ,_snake_case  :						List[Any]=None       ):





    '''simple docstring'''


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

    if config_path is not None:
        lowercase__		       =						WavLMConfig.from_pretrained(_snake_case       )
    else:
        lowercase__		       =						WavLMConfig()

    lowercase__		       =						WavLMModel(_snake_case       )

    recursively_load_weights(_snake_case     ,_snake_case       )

    hf_wavlm.save_pretrained(_snake_case       )


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)




