

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__	   = {
    """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__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Dict:




				'''simple docstring'''





				A__        =			["layers", "blocks"]
				for k in ignore_keys:
								state_dict.pop(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


lowerCAmelCase__	   = {
    """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__							(	SCREAMING_SNAKE_CASE_:							int      )			->      Optional[int]:




				'''simple docstring'''





				A__        =			list(s_dict.keys()      )
				for key in keys:
								A__        =			key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																A__        =			new_key.replace(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

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

								A__        =			s_dict.pop(SCREAMING_SNAKE_CASE_      )
				return s_dict



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      Union[str, Any]:




				'''simple docstring'''





				A__    ,				A__        =			emb.weight.shape
				A__        =			nn.Linear(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       bias=SCREAMING_SNAKE_CASE_      )
				A__        =			emb.weight.data
				return lin_layer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str      )			->      bytes:




				'''simple docstring'''





				os.makedirs(SCREAMING_SNAKE_CASE_	,       exist_ok=SCREAMING_SNAKE_CASE_      )
				A__        =			os.path.basename(SCREAMING_SNAKE_CASE_      )

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

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

				if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
								A__        =			open(SCREAMING_SNAKE_CASE_	,       "rb"      ).read()
								if hashlib.shaaaa(SCREAMING_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(SCREAMING_SNAKE_CASE_      ) as source, open(SCREAMING_SNAKE_CASE_	,       "wb"      ) as output:
								with tqdm(
								    total=int(source.info().get("Content-Length"      )      )	,       ncols=8_0	,       unit="iB"	,       unit_scale=SCREAMING_SNAKE_CASE_	,       unit_divisor=1_0_2_4      ) as loop:
												while True:
																A__        =			source.read(8_1_9_2      )
																if not buffer:
																				break

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

				A__        =			open(SCREAMING_SNAKE_CASE_	,       "rb"      ).read()
				if hashlib.shaaaa(SCREAMING_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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Optional[int]:




				'''simple docstring'''





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

				A__        =			WhisperConfig(
				    vocab_size=dimensions["n_vocab"]	,       encoder_ffn_dim=SCREAMING_SNAKE_CASE_	,       decoder_ffn_dim=SCREAMING_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"]	,       )

				A__        =			WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE_      )
				A__    ,				A__        =			model.model.load_state_dict(SCREAMING_SNAKE_CASE_	,       strict=SCREAMING_SNAKE_CASE_      )
				if len(SCREAMING_SNAKE_CASE_      ) > 0 and not set(SCREAMING_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:
								A__        =			make_linear_from_emb(model.model.decoder.embed_tokens      )
				else:
								A__        =			proj_out_weights

				model.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	lowerCAmelCase__	   = 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__	   = parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

