


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
	from huggingface_hub import snapshot_download
	from pyctcdecode import BeamSearchDecoderCTC

	from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
	from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
	from transformers import WavaVecaForCTC
@require_pyctcdecode
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
								__lowercase			       = dict(zip(snake_case_			,							range(len(snake_case_    )    )    )    )

								__lowercase			       = {
								    '''unk_token''': '''<unk>''',
								    '''bos_token''': '''<s>''',
								    '''eos_token''': '''</s>''',
								}
								__lowercase			       = {
								    '''feature_size''': 1,
								    '''padding_value''': 0.0,
								    '''sampling_rate''': 1_6_0_0_0,
								    '''return_attention_mask''': False,
								    '''do_normalize''': True,
								}

								__lowercase			       = tempfile.mkdtemp()
								__lowercase			       = os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']    )
								__lowercase			       = os.path.join(self.tmpdirname			,							snake_case_    )
								with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(snake_case_    ) + '''\n'''    )

								with open(self.feature_extraction_file			,							'''w'''			,							encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(snake_case_    ) + '''\n'''    )

								# load decoder from hub
								__lowercase			       = '''hf-internal-testing/ngram-beam-search-decoder'''






					def A   (					self			,							**snake_case_    ) ->      int:







								'''simple docstring'''

								__lowercase			       = self.add_kwargs_tokens_map.copy()
								kwargs.update(snake_case_    )
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							**snake_case_    ) ->      str:







								'''simple docstring'''

								return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

								return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,							**snake_case_    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								shutil.rmtree(self.tmpdirname    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								processor.save_pretrained(self.tmpdirname    )
								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname    )

								# tokenizer
								self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,							snake_case_    )

								# feature extractor
								self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,							snake_case_    )

								# decoder
								self.assertEqual(processor.decoder._alphabet.labels			,							decoder._alphabet.labels    )
								self.assertEqual(
								    processor.decoder.model_container[decoder._model_key]._unigram_set			,							decoder.model_container[decoder._model_key]._unigram_set			,							)
								self.assertIsInstance(processor.decoder			,							snake_case_    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = WavaVecaProcessorWithLM(
								    tokenizer=self.get_tokenizer()			,							feature_extractor=self.get_feature_extractor()			,							decoder=self.get_decoder()    )
								processor.save_pretrained(self.tmpdirname    )

								# make sure that error is thrown when decoder alphabet doesn't match
								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained(
								    self.tmpdirname			,							alpha=5.0			,							beta=3.0			,							score_boundary=-7.0			,							unk_score_offset=3    )

								# decoder
								self.assertEqual(processor.language_model.alpha			,							5.0    )
								self.assertEqual(processor.language_model.beta			,							3.0    )
								self.assertEqual(processor.language_model.score_boundary			,							-7.0    )
								self.assertEqual(processor.language_model.unk_score_offset			,							3    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.get_tokenizer()
								# add token to trigger raise
								tokenizer.add_tokens(['''xx''']    )
								with self.assertRaisesRegex(snake_case_			,							'''include'''    ):
											WavaVecaProcessorWithLM(
											    tokenizer=snake_case_			,							feature_extractor=self.get_feature_extractor()			,							decoder=self.get_decoder()    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = floats_list((3, 1_0_0_0)    )

								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''np'''    )
								__lowercase			       = processor(snake_case_			,							return_tensors='''np'''    )

								for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1e-2    )






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







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = '''This is a test string'''

								__lowercase			       = processor(text=snake_case_    )

								__lowercase			       = tokenizer(snake_case_    )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]    )






					def A   (					self			,							snake_case_=(2, 1_0, 1_6)			,							snake_case_=7_7    ) ->      int:







								'''simple docstring'''

								np.random.seed(snake_case_    )
								return np.random.rand(*snake_case_    )






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = self._get_dummy_logits(shape=(1_0, 1_6)			,							seed=1_3    )

								__lowercase			       = processor.decode(snake_case_    )

								__lowercase			       = decoder.decode_beams(snake_case_    )[0]

								self.assertEqual(decoded_decoder[0]			,							decoded_processor.text    )
								self.assertEqual('''</s> <s> </s>'''			,							decoded_processor.text    )
								self.assertEqual(decoded_decoder[-2]			,							decoded_processor.logit_score    )
								self.assertEqual(decoded_decoder[-1]			,							decoded_processor.lm_score    )






					@parameterized.expand([[None], ['''fork'''], ['''spawn''']]    )
					def A   (					self			,							snake_case_    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = self._get_dummy_logits()

								# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
								#       otherwise, the LM won't be available to the pool's sub-processes.
								# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
								if pool_context is None:
											__lowercase			       = processor.batch_decode(snake_case_    )
								else:
											with get_context(snake_case_    ).Pool() as pool:
														__lowercase			       = processor.batch_decode(snake_case_			,							snake_case_    )

								__lowercase			       = list(snake_case_    )

								with get_context('''fork'''    ).Pool() as p:
											__lowercase			       = decoder.decode_beams_batch(snake_case_			,							snake_case_    )

								__lowercase       ,			__lowercase       ,			__lowercase			       = [], [], []
								for beams in decoded_beams:
											texts_decoder.append(beams[0][0]    )
											logit_scores_decoder.append(beams[0][-2]    )
											lm_scores_decoder.append(beams[0][-1]    )

								self.assertListEqual(snake_case_			,							decoded_processor.text    )
								self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']			,							decoded_processor.text    )
								self.assertListEqual(snake_case_			,							decoded_processor.logit_score    )
								self.assertListEqual(snake_case_			,							decoded_processor.lm_score    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = self._get_dummy_logits()

								__lowercase			       = 1_5
								__lowercase			       = -2_0.0
								__lowercase			       = -4.0

								__lowercase			       = processor.batch_decode(
								    snake_case_			,							beam_width=snake_case_			,							beam_prune_logp=snake_case_			,							token_min_logp=snake_case_			,							)
								__lowercase			       = decoded_processor_out.text

								__lowercase			       = list(snake_case_    )

								with get_context('''fork'''    ).Pool() as pool:
											__lowercase			       = decoder.decode_beams_batch(
											    snake_case_			,							snake_case_			,							beam_width=snake_case_			,							beam_prune_logp=snake_case_			,							token_min_logp=snake_case_			,							)

								__lowercase			       = [d[0][0] for d in decoded_decoder_out]
								__lowercase			       = [d[0][2] for d in decoded_decoder_out]
								__lowercase			       = [d[0][3] for d in decoded_decoder_out]

								self.assertListEqual(snake_case_			,							snake_case_    )
								self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']			,							snake_case_    )

								self.assertTrue(np.array_equal(snake_case_			,							decoded_processor_out.logit_score    )    )
								self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]			,							snake_case_			,							atol=1e-3    )    )

								self.assertTrue(np.array_equal(snake_case_			,							decoded_processor_out.lm_score    )    )
								self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]			,							snake_case_			,							atol=1e-3    )    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								__lowercase			       = self._get_dummy_logits()

								__lowercase			       = 2.0
								__lowercase			       = 5.0
								__lowercase			       = -2_0.0
								__lowercase			       = True

								__lowercase			       = processor.batch_decode(
								    snake_case_			,							alpha=snake_case_			,							beta=snake_case_			,							unk_score_offset=snake_case_			,							lm_score_boundary=snake_case_			,							)
								__lowercase			       = decoded_processor_out.text

								__lowercase			       = list(snake_case_    )
								decoder.reset_params(
								    alpha=snake_case_			,							beta=snake_case_			,							unk_score_offset=snake_case_			,							lm_score_boundary=snake_case_			,							)

								with get_context('''fork'''    ).Pool() as pool:
											__lowercase			       = decoder.decode_beams_batch(
											    snake_case_			,							snake_case_			,							)

								__lowercase			       = [d[0][0] for d in decoded_decoder_out]

								self.assertListEqual(snake_case_			,							snake_case_    )
								self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']			,							snake_case_    )
								__lowercase			       = processor.decoder.model_container[processor.decoder._model_key]
								self.assertEqual(lm_model.alpha			,							2.0    )
								self.assertEqual(lm_model.beta			,							5.0    )
								self.assertEqual(lm_model.unk_score_offset			,							-2_0.0    )
								self.assertEqual(lm_model.score_boundary			,							snake_case_    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''    )

								__lowercase			       = processor.decoder.model_container[processor.decoder._model_key]
								__lowercase			       = Path(language_model._kenlm_model.path.decode('''utf-8'''    )    ).parent.parent.absolute()

								__lowercase			       = os.listdir(snake_case_    )
								__lowercase			       = ['''alphabet.json''', '''language_model''']

								downloaded_decoder_files.sort()
								expected_decoder_files.sort()

								# test that only decoder relevant files from
								# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
								# are downloaded and none of the rest (e.g. README.md, ...)
								self.assertListEqual(snake_case_			,							snake_case_    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = snapshot_download('''hf-internal-testing/processor_with_lm'''    )

								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained(snake_case_    )

								__lowercase			       = processor.decoder.model_container[processor.decoder._model_key]
								__lowercase			       = Path(language_model._kenlm_model.path.decode('''utf-8'''    )    ).parent.parent.absolute()

								__lowercase			       = os.listdir(snake_case_    )
								__lowercase			       = os.listdir(snake_case_    )

								local_decoder_files.sort()
								expected_decoder_files.sort()

								# test that both decoder form hub and local files in cache are the same
								self.assertListEqual(snake_case_			,							snake_case_    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''    )
								__lowercase			       = AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''    )

								__lowercase			       = floats_list((3, 1_0_0_0)    )

								__lowercase			       = processor_wavaveca(snake_case_			,							return_tensors='''np'''    )
								__lowercase			       = processor_auto(snake_case_			,							return_tensors='''np'''    )

								for key in input_wavaveca.keys():
											self.assertAlmostEqual(input_wavaveca[key].sum()			,							input_auto[key].sum()			,							delta=1e-2    )

								__lowercase			       = self._get_dummy_logits()

								__lowercase			       = processor_wavaveca.batch_decode(snake_case_    )
								__lowercase			       = processor_auto.batch_decode(snake_case_    )

								self.assertListEqual(decoded_wavaveca.text			,							decoded_auto.text    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.get_feature_extractor()
								__lowercase			       = self.get_tokenizer()
								__lowercase			       = self.get_decoder()

								__lowercase			       = WavaVecaProcessorWithLM(tokenizer=snake_case_			,							feature_extractor=snake_case_			,							decoder=snake_case_    )

								self.assertListEqual(
								    processor.model_input_names			,							feature_extractor.model_input_names			,							msg='''`processor` and `feature_extractor` model input names do not match'''			,							)






					@staticmethod
					def A   (					snake_case_			,							snake_case_    ) ->      int:







								'''simple docstring'''

								__lowercase			       = [d[key] for d in offsets]
								return retrieved_list






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''    )
								__lowercase			       = self._get_dummy_logits()[0]

								__lowercase			       = processor.decode(snake_case_			,							output_word_offsets=snake_case_    )
								# check Wav2Vec2CTCTokenizerOutput keys for word
								self.assertEqual(len(outputs.keys()    )			,							4    )
								self.assertTrue('''text''' in outputs    )
								self.assertTrue('''word_offsets''' in outputs    )
								self.assertTrue(isinstance(snake_case_			,							snake_case_    )    )

								self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']			,							'''word'''    )    )			,							outputs.text    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,							'''word'''    )			,							['''<s>''', '''<s>''', '''</s>''']    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,							'''start_offset'''    )			,							[0, 2, 4]    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,							'''end_offset'''    )			,							[1, 3, 5]    )






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''    )
								__lowercase			       = self._get_dummy_logits()

								__lowercase			       = processor.batch_decode(snake_case_			,							output_word_offsets=snake_case_    )

								# check Wav2Vec2CTCTokenizerOutput keys for word
								self.assertEqual(len(outputs.keys()    )			,							4    )
								self.assertTrue('''text''' in outputs    )
								self.assertTrue('''word_offsets''' in outputs    )
								self.assertTrue(isinstance(snake_case_			,							snake_case_    )    )

								self.assertListEqual(
								    [''' '''.join(self.get_from_offsets(snake_case_			,							'''word'''    )    ) for o in outputs['''word_offsets''']]			,							outputs.text    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,							'''word'''    )			,							['''<s>''', '''<s>''', '''</s>''']    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,							'''start_offset'''    )			,							[0, 2, 4]    )
								self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,							'''end_offset'''    )			,							[1, 3, 5]    )






					@slow
					@require_torch
					@require_torchaudio
					def A   (					self    ) ->      Any:







								'''simple docstring'''

								import torch

								__lowercase			       = load_dataset('''common_voice'''			,							'''en'''			,							split='''train'''			,							streaming=snake_case_    )
								__lowercase			       = ds.cast_column('''audio'''			,							datasets.Audio(sampling_rate=1_6_0_0_0    )    )
								__lowercase			       = iter(snake_case_    )
								__lowercase			       = next(snake_case_    )

								__lowercase			       = AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''    )
								__lowercase			       = WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''    )

								# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
								__lowercase			       = processor(sample['''audio''']['''array''']			,							return_tensors='''pt'''    ).input_values

								with torch.no_grad():
											__lowercase			       = model(snake_case_    ).logits.cpu().numpy()

								__lowercase			       = processor.decode(logits[0]			,							output_word_offsets=snake_case_    )

								__lowercase			       = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
								__lowercase			       = [
								    {
								        '''start_time''': d['''start_offset'''] * time_offset,
								        '''end_time''': d['''end_offset'''] * time_offset,
								        '''word''': d['''word'''],
								    }
								    for d in output['''word_offsets''']
								]

								__lowercase			       = '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

								# output words
								self.assertEqual(''' '''.join(self.get_from_offsets(snake_case_			,							'''word'''    )    )			,							snake_case_    )
								self.assertEqual(''' '''.join(self.get_from_offsets(snake_case_			,							'''word'''    )    )			,							output.text    )

								# output times
								__lowercase			       = torch.tensor(self.get_from_offsets(snake_case_			,							'''start_time'''    )    )
								__lowercase			       = torch.tensor(self.get_from_offsets(snake_case_			,							'''end_time'''    )    )

								# fmt: off
								__lowercase			       = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]    )
								__lowercase			       = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]    )
								# fmt: on

								self.assertTrue(torch.allclose(snake_case_			,							snake_case_			,							atol=0.0_1    )    )
								self.assertTrue(torch.allclose(snake_case_			,							snake_case_			,							atol=0.0_1    )    )






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


