


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


snake_case							: Union[str, 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 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  ['layers', 'blocks']
							for k in ignore_keys:
														state_dict.pop(__lowerCAmelCase      ,					__lowerCAmelCase     )


snake_case							: Tuple      =   {
    '''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 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							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(__lowerCAmelCase      ,					__lowerCAmelCase     )

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

														a__    =  s_dict.pop(__lowerCAmelCase     )
							return s_dict






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							a__   ,      a__    =  emb.weight.shape
							a__    =  nn.Linear(__lowerCAmelCase      ,					__lowerCAmelCase      ,					bias=__lowerCAmelCase     )
							a__    =  emb.weight.data
							return lin_layer






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       str     ):
							os.makedirs(__lowerCAmelCase      ,					exist_ok=__lowerCAmelCase     )
							a__    =  os.path.basename(__lowerCAmelCase     )

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

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

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

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

							a__    =  open(__lowerCAmelCase      ,					'rb'     ).read()
							if hashlib.shaaaa(__lowerCAmelCase     ).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 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       Any     ):
							if ".pt" not in checkpoint_path:
														a__    =  _download(_MODELS[checkpoint_path]     )
							else:
														a__    =  torch.load(__lowerCAmelCase      ,					map_location='cpu'     )
							a__    =  original_checkpoint['dims']
							a__    =  original_checkpoint['model_state_dict']
							a__    =  state_dict['decoder.token_embedding.weight']
							remove_ignore_keys_(__lowerCAmelCase     )
							rename_keys(__lowerCAmelCase     )
							a__    =  True
							a__    =  state_dict['decoder.layers.0.fc1.weight'].shape[0]

							a__    =  WhisperConfig(
							    vocab_size=dimensions['n_vocab']      ,					encoder_ffn_dim=__lowerCAmelCase      ,					decoder_ffn_dim=__lowerCAmelCase      ,					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(__lowerCAmelCase     )
							a__   ,      a__    =  model.model.load_state_dict(__lowerCAmelCase      ,					strict=__lowerCAmelCase     )
							if len(__lowerCAmelCase     ) > 0 and not set(__lowerCAmelCase     ) <= {
							    "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(__lowerCAmelCase     )


if __name__ == "__main__":
						snake_case							: Optional[int]      =   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.''')
						snake_case							: Optional[Any]      =   parser.parse_args()

						convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








from manim import *







class  snake_case_     (lowerCamelCase_   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[int]:
												a__    =  Rectangle(height=0.5    ,width=0.5   )
												a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0   )

												a__    =  Rectangle(height=0.25    ,width=0.25   )

												a__    =  [mem.copy() for i in range(6   )]
												a__    =  [mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('CPU'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												cpu.move_to([-2.5, -0.5, 0]   )
												self.add(__snake_case   )

												a__    =  [mem.copy() for i in range(4   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('GPU'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												gpu.move_to([-1, -1, 0]   )
												self.add(__snake_case   )

												a__    =  [mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )

												a__    =  Text('Model'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												model.move_to([3, -1.0, 0]   )
												self.add(__snake_case   )

												a__    =  []
												a__    =  []

												for i, rect in enumerate(__snake_case   ):
																			a__    =  fill.copy().set_fill(__snake_case    ,opacity=0.8   )
																			target.move_to(__snake_case   )
																			model_arr.append(__snake_case   )

																			a__    =  Rectangle(height=0.46    ,width=0.46   ).set_stroke(width=0.0   ).set_fill(__snake_case    ,opacity=0.8   )
																			cpu_target.move_to(cpu_left_col_base[i]   )
																			model_cpu_arr.append(__snake_case   )

												self.add(*__snake_case    ,*__snake_case   )

												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  [meta_mem.copy() for i in range(6   )]
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(*__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  VGroup(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0   )
												a__    =  Text('Disk'    ,font_size=24   )
												a__    =  Group(__snake_case    ,__snake_case   ).arrange(__snake_case    ,buff=0.5    ,aligned_edge=__snake_case   )
												disk.move_to([-4, -1.25, 0]   )
												self.add(__snake_case    ,__snake_case   )

												a__    =  Square(side_length=2.2   )
												key.move_to([-5, 2, 0]   )

												a__    =  MarkupText(
												    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'    ,font_size=18    ,)

												key_text.move_to([-5, 2.4, 0]   )

												self.add(__snake_case    ,__snake_case   )

												a__    =  MarkupText(
												    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'    ,font_size=18    ,)

												blue_text.next_to(__snake_case    ,DOWN * 2.4    ,aligned_edge=key_text.get_left()   )
												self.add(__snake_case   )

												a__    =  MarkupText(
												    F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case   )   )

												a__    =  Square(0.3   )
												input.set_fill(__snake_case    ,opacity=1.0   )
												input.set_stroke(width=0.0   )
												input.next_to(model_base[0]    ,__snake_case    ,buff=0.5   )

												self.play(Write(__snake_case   )   )

												input.generate_target()
												input.target.next_to(model_arr[0]    ,direction=__snake_case    ,buff=0.02   )
												self.play(MoveToTarget(__snake_case   )   )

												self.play(FadeOut(__snake_case   )   )

												a__    =  Arrow(start=__snake_case    ,end=__snake_case    ,color=__snake_case    ,buff=0.5   )
												a.next_to(model_arr[0].get_left()    ,__snake_case    ,buff=0.2   )

												model_cpu_arr[0].generate_target()
												model_cpu_arr[0].target.move_to(gpu_rect[0]   )

												a__    =  MarkupText(
												    F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'    ,font_size=24    ,)
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )   )

												a__    =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

												self.play(
												    Write(__snake_case   )    ,Circumscribe(model_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_cpu_arr[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)
												self.play(MoveToTarget(model_cpu_arr[0]   )   )

												a__    =  a.copy()
												for i in range(6   ):
																			a_c.next_to(model_arr[i].get_right() + 0.02    ,__snake_case    ,buff=0.2   )

																			input.generate_target()
																			input.target.move_to(model_arr[i].get_right() + 0.02   )

																			a__    =  AnimationGroup(
																			    FadeOut(__snake_case    ,run_time=0.5   )    ,MoveToTarget(__snake_case    ,run_time=0.5   )    ,FadeIn(__snake_case    ,run_time=0.5   )    ,lag_ratio=0.2   )

																			self.play(__snake_case   )

																			model_cpu_arr[i].generate_target()
																			model_cpu_arr[i].target.move_to(cpu_left_col_base[i]   )

																			if i < 5:
																										model_cpu_arr[i + 1].generate_target()
																										model_cpu_arr[i + 1].target.move_to(gpu_rect[0]   )
																										if i >= 1:
																																	a__    =  0.7

																										self.play(
																										    Circumscribe(model_arr[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i]    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(model_arr[i + 1]    ,color=__snake_case    ,**__snake_case   )    ,)
																										if i < 1:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]   )    ,MoveToTarget(model_cpu_arr[i + 1]   )    ,)
																										else:
																																	self.play(
																																	    MoveToTarget(model_cpu_arr[i]    ,run_time=0.7   )    ,MoveToTarget(model_cpu_arr[i + 1]    ,run_time=0.7   )    ,)
																			else:
																										model_cpu_arr[i].generate_target()
																										model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]   )
																										input.generate_target()
																										input.target.next_to(model_arr[-1].get_right()    ,RIGHT + 0.02    ,buff=0.2   )

																										self.play(
																										    Circumscribe(model_arr[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(cpu_left_col_base[-1]    ,color=__snake_case    ,**__snake_case   )    ,Circumscribe(gpu_rect[0]    ,color=__snake_case    ,**__snake_case   )    ,)

																										self.play(MoveToTarget(model_cpu_arr[i]   )   )

																			a__    =  a_c
																			a__    =  a_c.copy()

												input.generate_target()
												input.target.next_to(model_base[-1]    ,RIGHT + 0.02    ,buff=0.5   )
												self.play(
												    FadeOut(__snake_case   )    ,FadeOut(__snake_case    ,run_time=0.5   )    ,)

												a__    =  MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.'    ,font_size=24   )
												step_a.move_to([2, 2, 0]   )

												self.play(Write(__snake_case    ,run_time=3   )    ,MoveToTarget(__snake_case   )   )

												self.wait()




