




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_ : Optional[Any]    		=       logging.get_logger(__name__)

snake_case_ : int    		=       {
    "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",
    "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",
}
snake_case_ : List[str]    		=       [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def    A						(__A   :    Optional[int]   ,				__A   :    Any   ,				__A   :    Optional[int]   ,				__A   :    Optional[Any]   ,				__A   :    Union[str, Any]   ,				__A   :    str       )   ->      int:







     """simple docstring"""


     for attribute in key.split('''.'''       ):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    UpperCAmelCase_          =							'''lm_head'''

          UpperCAmelCase_          =							getattr(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       )

     if weight_type is not None:
          UpperCAmelCase_          =							getattr(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       ).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   :    Dict   ,				__A   :    Optional[Any]   ,				__A   :    Optional[Any]       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							fairseq_model.state_dict()

     UpperCAmelCase_          =							hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_          =							False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				hf_model.config.feat_extract_norm == '''group'''   ,				)
               UpperCAmelCase_          =							True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_          =							'''unispeech.''' + 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]:
                         UpperCAmelCase_          =							True
                         if "*" in mapped_key:
                              UpperCAmelCase_          =							name.split(SCREAMING_SNAKE_CASE_       )[0].split('''.'''       )[-2]
                              UpperCAmelCase_          =							mapped_key.replace('''*'''   ,				SCREAMING_SNAKE_CASE_       )
                         if "weight_g" in name:
                              UpperCAmelCase_          =							'''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_          =							'''weight_v'''
                         elif "bias" in name:
                              UpperCAmelCase_          =							'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCAmelCase_          =							'''weight'''
                         else:
                              UpperCAmelCase_          =							None
                         set_recursively(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_       )

     logger.warning(F"""Unused weights: {unused_weights}"""       )


def    A						(__A   :    Dict   ,				__A   :    int   ,				__A   :    List[Any]   ,				__A   :    Tuple   ,				__A   :    Union[str, Any]       )   ->      Union[str, Any]:







     """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(SCREAMING_SNAKE_CASE_       )


@torch.no_grad()
def    A						(__A   :    Tuple   ,				__A   :    Union[str, Any]   ,				__A   :    Tuple=None   ,				__A   :    List[Any]=None   ,				__A   :    List[str]=True       )   ->      str:







     """simple docstring"""


     if config_path is not None:
          UpperCAmelCase_          =							UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
     else:
          UpperCAmelCase_          =							UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_          =							Dictionary.load_from_json(SCREAMING_SNAKE_CASE_       )

               # 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.eos_index
               UpperCAmelCase_          =							len(target_dict.symbols       )
               UpperCAmelCase_          =							os.path.join(SCREAMING_SNAKE_CASE_   ,				'''vocab.json'''       )
               if not os.path.isdir(SCREAMING_SNAKE_CASE_       ):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE_       )       )
                    return
               os.makedirs(SCREAMING_SNAKE_CASE_   ,				exist_ok=SCREAMING_SNAKE_CASE_       )
               UpperCAmelCase_          =							target_dict.indices

               # fairseq has the <pad> and <s> switched
               UpperCAmelCase_          =							42
               UpperCAmelCase_          =							43
               with open(SCREAMING_SNAKE_CASE_   ,				'''w'''   ,				encoding='''utf-8'''       ) as vocab_handle:
                    json.dump(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       )
               UpperCAmelCase_          =							WavaVecaPhonemeCTCTokenizer(
                   SCREAMING_SNAKE_CASE_   ,				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=SCREAMING_SNAKE_CASE_   ,				)
               UpperCAmelCase_          =							True if config.feat_extract_norm == '''layer''' else False
               UpperCAmelCase_          =							WavaVecaFeatureExtractor(
                   feature_size=1   ,				sampling_rate=16000   ,				padding_value=0   ,				do_normalize=SCREAMING_SNAKE_CASE_   ,				return_attention_mask=SCREAMING_SNAKE_CASE_   ,				)
               UpperCAmelCase_          =							WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_   ,				tokenizer=SCREAMING_SNAKE_CASE_       )
               processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

          UpperCAmelCase_          =							UniSpeechForCTC(SCREAMING_SNAKE_CASE_       )
     else:
          UpperCAmelCase_          =							UniSpeechForPreTraining(SCREAMING_SNAKE_CASE_       )

     if is_finetuned:
          UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]   ,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       ), '''w2v_path''': checkpoint_path}       )
     else:
          UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

     UpperCAmelCase_          =							model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       )

     hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
    snake_case_ : Union[str, Any]    		=       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_ : int    		=       parser.parse_args()
    convert_unispeech_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 					lowerCAmelCase	(					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:
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'height': 18, 'width': 18}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize







		def 							__A						(	self				)     ->							Optional[Any]:
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
								            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
								        ]				),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}





@require_torch
@require_vision
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ImageGPTImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					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 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'height': 18, 'width': 18}				)

								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict						,       size=42				)
								self.assertEqual(image_processor.size						,       {'height': 42, 'width': 42}				)







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

								with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       'image_processor.json'				)
														image_processor_first.to_json_file(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_json_file(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					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 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_pretrained(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					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 							__A						(	self				)     ->							Optional[Any]:
								pass





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					load_dataset('hf-internal-testing/fixtures_image_utils' ,			split='test'  )

						SCREAMING_SNAKE_CASE													=					Image.open(dataset[4]['file']  )
						SCREAMING_SNAKE_CASE													=					Image.open(dataset[5]['file']  )

						SCREAMING_SNAKE_CASE													=					[imagea, imagea]

						return images


@require_vision
@require_torch
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

								SCREAMING_SNAKE_CASE													=					prepare_images()

								# test non-batched
								SCREAMING_SNAKE_CASE													=					image_processing(images[0]						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (1, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[306, 191, 191]
								self.assertEqual(encoding.input_ids[0, :3].tolist()						,       lowerCAmelCase__				)

								# test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (2, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[303, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()						,       lowerCAmelCase__				)


