
"""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__				=					{
    '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			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			['''layers''', '''blocks''']
		for k in ignore_keys:
				state_dict.pop(__a,				__a     )


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			(			__a     ):
		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(__a,				__a     )

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

				SCREAMING_SNAKE_CASE_    			=			s_dict.pop(__a     )
		return s_dict



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			emb.weight.shape
		SCREAMING_SNAKE_CASE_    			=			nn.Linear(__a,				__a,				bias=__a     )
		SCREAMING_SNAKE_CASE_    			=			emb.weight.data
		return lin_layer



def 							_lowerCamelCase			(			__a,				__a     ):
		os.makedirs(__a,				exist_ok=__a     )
		SCREAMING_SNAKE_CASE_    			=			os.path.basename(__a     )

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

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

		if os.path.isfile(__a     ):
				SCREAMING_SNAKE_CASE_    			=			open(__a,				'''rb'''     ).read()
				if hashlib.shaaaa(__a     ).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(__a     ) as source, open(__a,				'''wb'''     ) as output:
				with tqdm(
				    total=int(source.info().get('''Content-Length'''     )     ),				ncols=80,				unit='''iB''',				unit_scale=__a,				unit_divisor=1_024     ) as loop:
						while True:
								SCREAMING_SNAKE_CASE_    			=			source.read(8_192     )
								if not buffer:
										break

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

		SCREAMING_SNAKE_CASE_    			=			open(__a,				'''rb'''     ).read()
		if hashlib.shaaaa(__a     ).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			(			__a,				__a     ):
		if ".pt" not in checkpoint_path:
				SCREAMING_SNAKE_CASE_    			=			_download(_MODELS[checkpoint_path]     )
		else:
				SCREAMING_SNAKE_CASE_    			=			torch.load(__a,				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_(__a     )
		rename_keys(__a     )
		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=__a,				decoder_ffn_dim=__a,				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(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			model.model.load_state_dict(__a,				strict=__a     )
		if len(__a     ) > 0 and not set(__a     ) <= {
		    "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(__a     )


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)

"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a = 100,				):
		SCREAMING_SNAKE_CASE_    			=			x_start
		SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
		SCREAMING_SNAKE_CASE_    			=			0.0
		for _ in range(__a     ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				SCREAMING_SNAKE_CASE_    			=			(x_end - x_start) / steps + xa
				SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
				area += abs(fxa + fxa     ) * (xa - xa) / 2
				# Increment step
				SCREAMING_SNAKE_CASE_    			=			xa
				SCREAMING_SNAKE_CASE_    			=			fxa
		return area


if __name__ == "__main__":



						def 							_lowerCamelCase			(			__a     ):
								return x**3 + x**2

						print('f(x) = x^3 + x^2')
						print('The area between the curve, x = -5, x = 5 and the x axis is:')
						lowerCAmelCase__				=					10
						while i <= 100000:
												print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
												i *= 10