




"""simple docstring"""




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


lowerCAmelCase							:    Optional[Any]  							=							{
    """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 					a__				(       snake_case__       )		->      Optional[int]:
			lowerCamelCase       =      ["""layers""", """blocks"""]
			for k in ignore_keys:
						state_dict.pop(snake_case__ ,				snake_case__       )


lowerCAmelCase							:    Dict  							=							{
    """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 					a__				(       snake_case__       )		->      Tuple:
			lowerCamelCase       =      list(s_dict.keys()       )
			for key in keys:
						lowerCamelCase       =      key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowerCamelCase       =      new_key.replace(snake_case__ ,				snake_case__       )

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

						lowerCamelCase       =      s_dict.pop(snake_case__       )
			return s_dict



def 					a__				(       snake_case__       )		->      Dict:
			lowerCamelCase   ,     lowerCamelCase       =      emb.weight.shape
			lowerCamelCase       =      nn.Linear(snake_case__ ,				snake_case__ ,				bias=snake_case__       )
			lowerCamelCase       =      emb.weight.data
			return lin_layer



def 					a__				(       snake_case__ ,				snake_case__       )		->      bytes:
			os.makedirs(snake_case__ ,				exist_ok=snake_case__       )
			lowerCamelCase       =      os.path.basename(snake_case__       )

			lowerCamelCase       =      url.split("""/"""       )[-2]
			lowerCamelCase       =      os.path.join(snake_case__ ,				snake_case__       )

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

			if os.path.isfile(snake_case__       ):
						lowerCamelCase       =      open(snake_case__ ,				"""rb"""       ).read()
						if hashlib.shaaaa(snake_case__       ).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(snake_case__       ) as source, open(snake_case__ ,				"""wb"""       ) as output:
						with tqdm(
						    total=int(source.info().get("""Content-Length"""       )       ) ,				ncols=80 ,				unit="""iB""" ,				unit_scale=snake_case__ ,				unit_divisor=10_24       ) as loop:
									while True:
												lowerCamelCase       =      source.read(81_92       )
												if not buffer:
															break

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

			lowerCamelCase       =      open(snake_case__ ,				"""rb"""       ).read()
			if hashlib.shaaaa(snake_case__       ).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 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			if ".pt" not in checkpoint_path:
						lowerCamelCase       =      _download(_MODELS[checkpoint_path]       )
			else:
						lowerCamelCase       =      torch.load(snake_case__ ,				map_location="""cpu"""       )
			lowerCamelCase       =      original_checkpoint["""dims"""]
			lowerCamelCase       =      original_checkpoint["""model_state_dict"""]
			lowerCamelCase       =      state_dict["""decoder.token_embedding.weight"""]
			remove_ignore_keys_(snake_case__       )
			rename_keys(snake_case__       )
			lowerCamelCase       =      True
			lowerCamelCase       =      state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

			lowerCamelCase       =      WhisperConfig(
			    vocab_size=dimensions["""n_vocab"""] ,				encoder_ffn_dim=snake_case__ ,				decoder_ffn_dim=snake_case__ ,				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"""] ,				)

			lowerCamelCase       =      WhisperForConditionalGeneration(snake_case__       )
			lowerCamelCase   ,     lowerCamelCase       =      model.model.load_state_dict(snake_case__ ,				strict=snake_case__       )
			if len(snake_case__       ) > 0 and not set(snake_case__       ) <= {
			    "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:
						lowerCamelCase       =      make_linear_from_emb(model.model.decoder.embed_tokens       )
			else:
						lowerCamelCase       =      proj_out_weights

			model.save_pretrained(snake_case__       )


if __name__ == "__main__":
			lowerCAmelCase							:    List[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.""")
			lowerCAmelCase							:    int  							=							parser.parse_args()

			convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



