

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_ :				Dict						 =    logging.get_logger(__name__)

A_ :				str						 =    {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					_a		(_UpperCAmelCase	,     _UpperCAmelCase       ):


     '''simple docstring'''






     UpperCAmelCase__:     str													= '''swin'''

     UpperCAmelCase__:     Optional[int]													= {
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }






     def __init__(				self					, A__=224					, A__=4					, A__=3					, A__=96					, A__=[2, 2, 6, 2]					, A__=[3, 6, 12, 24]					, A__=7					, A__=4.0					, A__=True					, A__=0.0					, A__=0.0					, A__=0.1					, A__="gelu"					, A__=False					, A__=0.0_2					, A__=1e-5					, A__=32					, A__=None					, A__=None					, **A__					, ):
      super().__init__(**A_    )

      A__							:					List[Any]       =	image_size
      A__							:					int       =	patch_size
      A__							:					Optional[int]       =	num_channels
      A__							:					int       =	embed_dim
      A__							:					List[str]       =	depths
      A__							:					Optional[int]       =	len(A_    )
      A__							:					Union[str, Any]       =	num_heads
      A__							:					List[str]       =	window_size
      A__							:					Optional[int]       =	mlp_ratio
      A__							:					Tuple       =	qkv_bias
      A__							:					Union[str, Any]       =	hidden_dropout_prob
      A__							:					List[Any]       =	attention_probs_dropout_prob
      A__							:					Tuple       =	drop_path_rate
      A__							:					Optional[int]       =	hidden_act
      A__							:					Optional[int]       =	use_absolute_embeddings
      A__							:					Dict       =	layer_norm_eps
      A__							:					List[str]       =	initializer_range
      A__							:					Dict       =	encoder_stride
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      A__							:					Optional[Any]       =	int(embed_dim * 2 ** (len(A_    ) - 1)    )
      A__							:					List[Any]       =	["""stem"""] + [F"""stage{idx}""" for idx in range(1					, len(A_    ) + 1    )]
      A__     ,     A__							:					int       =	get_aligned_output_features_output_indices(
          out_features=A_					, out_indices=A_					, stage_names=self.stage_names    )



class 					_a		(_UpperCAmelCase       ):


     '''simple docstring'''






     UpperCAmelCase__:     Union[str, Any]													= version.parse('''1.11'''       )






     @property
     def 						__A    (				self    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )






     @property
     def 						__A    (				self    ):
      return 1e-4






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 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   Optional[int]:
	A__							:					List[Any]       =	["""layers""", """blocks"""]
	for k in ignore_keys:
		state_dict.pop(lowercase_					,   lowercase_      )


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 		UpperCamelCase      (lowercase_:				str      )  ->   Any:
	A__							:					Dict       =	list(s_dict.keys()      )
	for key in keys:
		A__							:					List[str]       =	key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				A__							:					List[Any]       =	new_key.replace(lowercase_					,   lowercase_      )

		print(f"""{key} -> {new_key}"""      )

		A__							:					Tuple       =	s_dict.pop(lowercase_      )
	return s_dict

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   Optional[int]:
	A__     ,     A__							:					Any       =	emb.weight.shape
	A__							:					str       =	nn.Linear(lowercase_					,   lowercase_					,   bias=lowercase_      )
	A__							:					Union[str, Any]       =	emb.weight.data
	return lin_layer

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   bytes:
	os.makedirs(lowercase_					,   exist_ok=lowercase_      )
	A__							:					Tuple       =	os.path.basename(lowercase_      )

	A__							:					int       =	url.split("""/"""      )[-2]
	A__							:					Dict       =	os.path.join(lowercase_					,   lowercase_      )

	if os.path.exists(lowercase_      ) and not os.path.isfile(lowercase_      ):
		raise RuntimeError(f"""{download_target} exists and is not a regular file"""      )

	if os.path.isfile(lowercase_      ):
		A__							:					Optional[Any]       =	open(lowercase_					,   """rb"""      ).read()
		if hashlib.shaaaa(lowercase_      ).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(lowercase_      ) as source, open(lowercase_					,   """wb"""      ) as output:
		with tqdm(
		    total=int(source.info().get("""Content-Length"""      )      )					,   ncols=80					,   unit="""iB"""					,   unit_scale=lowercase_					,   unit_divisor=1024      ) as loop:
			while True:
				A__							:					Any       =	source.read(8192      )
				if not buffer:
					break

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

	A__							:					Dict       =	open(lowercase_					,   """rb"""      ).read()
	if hashlib.shaaaa(lowercase_      ).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 		UpperCamelCase      (lowercase_:				List[Any]					,   lowercase_:				Tuple      )  ->   Optional[Any]:
	if ".pt" not in checkpoint_path:
		A__							:					Tuple       =	_download(_MODELS[checkpoint_path]      )
	else:
		A__							:					Optional[int]       =	torch.load(lowercase_					,   map_location="""cpu"""      )
	A__							:					str       =	original_checkpoint["""dims"""]
	A__							:					List[Any]       =	original_checkpoint["""model_state_dict"""]
	A__							:					Optional[Any]       =	state_dict["""decoder.token_embedding.weight"""]
	remove_ignore_keys_(lowercase_      )
	rename_keys(lowercase_      )
	A__							:					List[str]       =	True
	A__							:					Optional[Any]       =	state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

	A__							:					List[Any]       =	WhisperConfig(
	    vocab_size=dimensions["""n_vocab"""]					,   encoder_ffn_dim=lowercase_					,   decoder_ffn_dim=lowercase_					,   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__							:					Optional[Any]       =	WhisperForConditionalGeneration(lowercase_      )
	A__     ,     A__							:					List[Any]       =	model.model.load_state_dict(lowercase_					,   strict=lowercase_      )
	if len(lowercase_      ) > 0 and not set(lowercase_      ) <= {
	    "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__							:					Any       =	make_linear_from_emb(model.model.decoder.embed_tokens      )
	else:
		A__							:					str       =	proj_out_weights

	model.save_pretrained(lowercase_      )


if __name__ == "__main__":
	A_ :				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_ :				Tuple						 =    parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



