






import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


a_		:						Dict             =    {
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ['layers', 'blocks']
			for k in ignore_keys:
						state_dict.pop(_UpperCAmelCase							,       _UpperCAmelCase)


a_		:						Any             =    {
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= list(s_dict.keys())
			for key in keys:
						SCREAMING_SNAKE_CASE						= key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												SCREAMING_SNAKE_CASE						= new_key.replace(_UpperCAmelCase							,       _UpperCAmelCase)

						print(F'''{key} -> {new_key}''')

						SCREAMING_SNAKE_CASE						= s_dict.pop(_UpperCAmelCase)
			return s_dict






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= emb.weight.shape
			SCREAMING_SNAKE_CASE						= nn.Linear(_UpperCAmelCase							,       _UpperCAmelCase							,       bias=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= emb.weight.data
			return lin_layer






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			os.makedirs(_UpperCAmelCase							,       exist_ok=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= os.path.basename(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= url.split('/')[-2]
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       _UpperCAmelCase)

			if os.path.exists(_UpperCAmelCase) and not os.path.isfile(_UpperCAmelCase):
						raise RuntimeError(F'''{download_target} exists and is not a regular file''')

			if os.path.isfile(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= open(_UpperCAmelCase							,       'rb').read()
						if hashlib.shaaaa(_UpperCAmelCase).hexdigest() == expected_shaaaa:
									return model_bytes
						else:
									warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''')

			with urllib.request.urlopen(_UpperCAmelCase) as source, open(_UpperCAmelCase							,       'wb') as output:
						with tqdm(
						    total=int(source.info().get('Content-Length'))							,       ncols=80							,       unit='iB'							,       unit_scale=_UpperCAmelCase							,       unit_divisor=1024) as loop:
									while True:
												SCREAMING_SNAKE_CASE						= source.read(8192)
												if not buffer:
															break

												output.write(_UpperCAmelCase)
												loop.update(len(_UpperCAmelCase))

			SCREAMING_SNAKE_CASE						= open(_UpperCAmelCase							,       'rb').read()
			if hashlib.shaaaa(_UpperCAmelCase).hexdigest() != expected_shaaaa:
						raise RuntimeError(
						    'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.')

			return model_bytes






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if ".pt" not in checkpoint_path:
						SCREAMING_SNAKE_CASE						= _download(_MODELS[checkpoint_path])
			else:
						SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location='cpu')
			SCREAMING_SNAKE_CASE						= original_checkpoint['dims']
			SCREAMING_SNAKE_CASE						= original_checkpoint['model_state_dict']
			SCREAMING_SNAKE_CASE						= state_dict['decoder.token_embedding.weight']
			remove_ignore_keys_(_UpperCAmelCase)
			rename_keys(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= True
			SCREAMING_SNAKE_CASE						= state_dict['decoder.layers.0.fc1.weight'].shape[0]

			SCREAMING_SNAKE_CASE						= WhisperConfig(
			    vocab_size=dimensions['n_vocab']							,       encoder_ffn_dim=_UpperCAmelCase							,       decoder_ffn_dim=_UpperCAmelCase							,       num_mel_bins=dimensions['n_mels']							,       d_model=dimensions['n_audio_state']							,       max_target_positions=dimensions['n_text_ctx']							,       encoder_layers=dimensions['n_audio_layer']							,       encoder_attention_heads=dimensions['n_audio_head']							,       decoder_layers=dimensions['n_text_layer']							,       decoder_attention_heads=dimensions['n_text_state']							,       max_source_positions=dimensions['n_audio_ctx']							,       )

			SCREAMING_SNAKE_CASE						= WhisperForConditionalGeneration(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model.model.load_state_dict(_UpperCAmelCase							,       strict=_UpperCAmelCase)
			if len(_UpperCAmelCase) > 0 and not set(_UpperCAmelCase) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
						    F''' but all the following weights are missing {missing}''')

			if tie_embeds:
						SCREAMING_SNAKE_CASE						= make_linear_from_emb(model.model.decoder.embed_tokens)
			else:
						SCREAMING_SNAKE_CASE						= proj_out_weights

			model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						Optional[Any]             =    argparse.ArgumentParser()
				# # Required parameters
				parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				a_		:						str             =    parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




