

'''simple docstring'''





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__snake_case	: List[Any]       				=							logging.get_logger(__name__)

__snake_case	: Dict       				=							{
    "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_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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__snake_case	: List[str]       				=							[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]



def 					_lowercase						(   lowerCamelCase__			:		Any,		lowerCamelCase__			:		Any,		lowerCamelCase__			:		Optional[Any],		lowerCamelCase__			:		List[Any],		lowerCamelCase__			:		Optional[int]							):
				for attribute in key.split("."							):
								_a        =   getattr(lowerCamelCase__,		lowerCamelCase__							)

				if weight_type is not None:
								_a        =   getattr(lowerCamelCase__,		lowerCamelCase__							).shape
				else:
								_a        =   hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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":
								_a        =   value
				elif weight_type == "weight_g":
								_a        =   value
				elif weight_type == "weight_v":
								_a        =   value
				elif weight_type == "bias":
								_a        =   value
				else:
								_a        =   value

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



def 					_lowercase						(   lowerCamelCase__			:		Dict,		lowerCamelCase__			:		Optional[Any]							):
				_a        =   []
				_a        =   fairseq_model.state_dict()

				_a        =   hf_model.unispeech_sat.feature_extractor

				for name, value in fairseq_dict.items():
								_a        =   False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		hf_model.config.feat_extract_norm == "group",		)
												_a        =   True
								else:
												for key, mapped_key in MAPPING.items():
																_a        =   """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
																				if "layer_norm_for_extract" in name and (".".join(name.split("."							)[:-1]							) != key):
																								# special case since naming is very similar
																								continue
																				_a        =   True
																				if "*" in mapped_key:
																								_a        =   name.split(lowerCamelCase__							)[0].split("."							)[-2]
																								_a        =   mapped_key.replace("*",		lowerCamelCase__							)
																				if "weight_g" in name:
																								_a        =   """weight_g"""
																				elif "weight_v" in name:
																								_a        =   """weight_v"""
																				elif "bias" in name:
																								_a        =   """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_a        =   """weight"""
																				else:
																								_a        =   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__			:		str,		lowerCamelCase__			:		Dict,		lowerCamelCase__			:		Optional[Any],		lowerCamelCase__			:		int,		lowerCamelCase__			:		int							):
				_a        =   full_name.split("conv_layers."							)[-1]
				_a        =   name.split("."							)
				_a        =   int(items[0]							)
				_a        =   int(items[1]							)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
												_a        =   value
												logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
												_a        =   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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''							)
												_a        =   value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F'''{full_name} has size {value.shape}, but'''
																    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''							)
												_a        =   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__			:		List[Any],		lowerCamelCase__			:		Optional[int],		lowerCamelCase__			:		int=None,		lowerCamelCase__			:		Tuple=None,		lowerCamelCase__			:		Optional[Any]=True							):
				if config_path is not None:
								_a        =   UniSpeechSatConfig.from_pretrained(lowerCamelCase__							)
				else:
								_a        =   UniSpeechSatConfig()

				_a        =   """"""

				if is_finetuned:
								_a        =   UniSpeechSatForCTC(lowerCamelCase__							)
				else:
								_a        =   UniSpeechSatForPreTraining(lowerCamelCase__							)

				_a        =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path],		arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
				_a        =   model[0].eval()

				recursively_load_weights(lowerCamelCase__,		lowerCamelCase__							)

				hf_wavavec.save_pretrained(lowerCamelCase__							)


if __name__ == "__main__":
							__snake_case	: Tuple       				=							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(
							    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							__snake_case	: str       				=							parser.parse_args()
							convert_unispeech_sat_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


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


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)