



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase     							=						logging.get_logger(__name__)






def      _A				(UpperCamelCase		:      Union[tf.Tensor, np.ndarray] )  ->Dict:



			'''simple docstring'''







			if isinstance(SCREAMING_SNAKE_CASE__      ,			np.ndarray ):
						return list(tensor.shape )

			lowerCamelCase__ :      Optional[int]								=		tf.shape(SCREAMING_SNAKE_CASE__ )

			if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__ ):
						return dynamic

			lowerCamelCase__ :      List[str]								=		tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__ )]






def      _A				(UpperCamelCase		:      tf.Tensor      ,			UpperCamelCase		:      Optional[int] = None      ,			UpperCamelCase		:      Optional[str] = None )  ->Union[str, Any]:



			'''simple docstring'''







			return tf.nn.softmax(logits=logits + 1E-9      ,			axis=SCREAMING_SNAKE_CASE__      ,			name=SCREAMING_SNAKE_CASE__ )






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Optional[Any]=1E-5      ,			UpperCamelCase		:      Union[str, Any]=-1 )  ->str:



			'''simple docstring'''







			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ ):
						raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )

			# Get mean and variance on the axis to be normalized
			lowerCamelCase__ :      str								=		tf.nn.moments(SCREAMING_SNAKE_CASE__      ,			axes=[axis]      ,			keepdims=SCREAMING_SNAKE_CASE__ )

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						lowerCamelCase__ :      Optional[Any]								=		[1] * inputs.shape.rank
						lowerCamelCase__ :      Any								=		shape_list(SCREAMING_SNAKE_CASE__ )[axis]
						lowerCamelCase__ :      List[str]								=		tf.reshape(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ )
						lowerCamelCase__ :      List[Any]								=		tf.reshape(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ )

			# Compute layer normalization using the batch_normalization
			# function.
			lowerCamelCase__ :      List[Any]								=		tf.nn.batch_normalization(
			    SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			offset=SCREAMING_SNAKE_CASE__      ,			scale=SCREAMING_SNAKE_CASE__      ,			variance_epsilon=SCREAMING_SNAKE_CASE__      ,			)
			return outputs






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Dict=0      ,			UpperCamelCase		:      Dict=-1 )  ->Any:



			'''simple docstring'''







			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			lowerCamelCase__ :      int								=		tf.shape(SCREAMING_SNAKE_CASE__ )
			lowerCamelCase__ :      str								=		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
			lowerCamelCase__ :      List[str]								=		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,			axis=0 )
			return tf.reshape(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ )






def      _A				(UpperCamelCase		:      tf.Tensor )  ->List[str]:



			'''simple docstring'''







			if not isinstance(SCREAMING_SNAKE_CASE__      ,			tf.Tensor ):
						lowerCamelCase__ :      Optional[Any]								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						lowerCamelCase__ :      Optional[int]								=		encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						lowerCamelCase__ :      List[Any]								=		encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			lowerCamelCase__ :      int								=		(
			    tf.cast(1      ,			encoder_attention_mask.dtype ) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask






def      _A				(UpperCamelCase		:      tf.Tensor      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      str = "input_ids" )  ->Optional[int]:



			'''simple docstring'''







			tf.debugging.assert_less(
			    SCREAMING_SNAKE_CASE__      ,			tf.cast(SCREAMING_SNAKE_CASE__      ,			dtype=tensor.dtype )      ,			message=(
			        f"The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__ )}) must be smaller than the embedding "
			        f"layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )      ,			)






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      int )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		64512
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			lowerCamelCase__ :      Optional[int]								=		[x for x in data if len(SCREAMING_SNAKE_CASE__ ) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    """The following attributes cannot be saved to HDF5 file because """
						    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    f"bytes: {bad_attributes}" )

			lowerCamelCase__ :      str								=		np.asarray(SCREAMING_SNAKE_CASE__ )

			lowerCamelCase__ :      Union[str, Any]								=		1
			lowerCamelCase__ :      Optional[Any]								=		np.array_split(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ )

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
						num_chunks += 1
						lowerCamelCase__ :      Any								=		np.array_split(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ )

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__ ):
									lowerCamelCase__ :      List[Any]								=		chunk_data
			else:
						lowerCamelCase__ :      Union[str, Any]								=		data






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      Dict )  ->int:



			'''simple docstring'''







			if name in group.attrs:
						lowerCamelCase__ :      Tuple								=		[n.decode("""utf8""" ) if hasattr(SCREAMING_SNAKE_CASE__      ,			"""decode""" ) else n for n in group.attrs[name]]
			else:
						lowerCamelCase__ :      Tuple								=		[]
						lowerCamelCase__ :      Optional[Any]								=		0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode("""utf8""" ) if hasattr(SCREAMING_SNAKE_CASE__      ,			"""decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
									chunk_id += 1
			return data






def      _A				(UpperCamelCase		:      List[str] )  ->Dict:



			'''simple docstring'''







			def _expand_single_ad_tensor(UpperCamelCase		:      Optional[Any] ):
						if isinstance(SCREAMING_SNAKE_CASE__      ,			tf.Tensor ) and t.shape.rank == 1:
									return tf.expand_dims(SCREAMING_SNAKE_CASE__      ,			axis=-1 )
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor      ,			SCREAMING_SNAKE_CASE__ )








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


_lowercase     							=						{
    '''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				(UpperCamelCase		:      Any )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		["""layers""", """blocks"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase      ,			UpperCamelCase )


_lowercase     							=						{
    '''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				(UpperCamelCase		:      Union[str, Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		list(s_dict.keys() )
			for key in keys:
						lowerCamelCase__ :      Dict								=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowerCamelCase__ :      Tuple								=		new_key.replace(UpperCamelCase      ,			UpperCamelCase )

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

						lowerCamelCase__ :      int								=		s_dict.pop(UpperCamelCase )
			return s_dict






def      _A				(UpperCamelCase		:      List[Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      int								=		emb.weight.shape
			lowerCamelCase__ :      Optional[int]								=		nn.Linear(UpperCamelCase      ,			UpperCamelCase      ,			bias=UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		emb.weight.data
			return lin_layer






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->bytes:



			'''simple docstring'''







			os.makedirs(UpperCamelCase      ,			exist_ok=UpperCamelCase )
			lowerCamelCase__ :      Any								=		os.path.basename(UpperCamelCase )

			lowerCamelCase__ :      int								=		url.split("""/""" )[-2]
			lowerCamelCase__ :      Optional[int]								=		os.path.join(UpperCamelCase      ,			UpperCamelCase )

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

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

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

			lowerCamelCase__ :      Any								=		open(UpperCamelCase      ,			"""rb""" ).read()
			if hashlib.shaaaa(UpperCamelCase ).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				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







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

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

			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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.''')
				_lowercase     							=						parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



