"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase      : List[Any]       =logging.get_logger(__name__)






class 							__snake_case(					_lowerCAmelCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			["input_values", "attention_mask"]
    def __init__(  self					,       __lowerCamelCase=1					,       __lowerCamelCase=16000					,       __lowerCamelCase=0.0					,       __lowerCamelCase=False					,       __lowerCamelCase=True					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         super().__init__(feature_size=lowerCAmelCase__					,       sampling_rate=lowerCAmelCase__					,       padding_value=lowerCAmelCase__					,       **lowerCAmelCase__	)
         __A					:					Any								=		return_attention_mask
         __A					:					Optional[Any]								=		do_normalize
    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase = 0.0	):




         '''simple docstring'''



         if attention_mask is not None:
              __A					:					str								=		np.array(lowerCAmelCase__					,       np.intaa	)
              __A					:					Optional[int]								=		[]

              for vector, length in zip(lowerCAmelCase__					,       attention_mask.sum(-1	)	):
                   __A					:					Union[str, Any]								=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
                   if length < normed_slice.shape[0]:
                        __A					:					Dict								=		padding_value

                   normed_input_values.append(lowerCAmelCase__	)
         else:
              __A					:					Any								=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

         return normed_input_values

    def __call__(  self					,       __lowerCamelCase					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                       F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                       F' {self.sampling_rate} and not {sampling_rate}.'	)
         else:
              logger.warning(
                  'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                  'Failing to do so can result in silent errors that might be hard to debug.'	)

         __A					:					Optional[int]								=		isinstance(lowerCAmelCase__					,       np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
              raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
         __A					:					Any								=		is_batched_numpy or (
             isinstance(lowerCAmelCase__					,       (list, tuple)	) and (isinstance(raw_speech[0]					,       (np.ndarray, tuple, list)	))
         )

         # always return batch
         if not is_batched:
              __A					:					List[str]								=		[raw_speech]

         # convert into correct format for padding
         __A					:					Tuple								=		BatchFeature({'input_values': raw_speech}	)

         __A					:					Optional[int]								=		self.pad(
             lowerCAmelCase__					,       padding=lowerCAmelCase__					,       max_length=lowerCAmelCase__					,       truncation=lowerCAmelCase__					,       pad_to_multiple_of=lowerCAmelCase__					,       return_attention_mask=lowerCAmelCase__					,       )

         # convert input values to correct format
         __A					:					Optional[Any]								=		padded_inputs['''input_values''']
         if not isinstance(input_values[0]					,       np.ndarray	):
              __A					:					Tuple								=		[np.asarray(lowerCAmelCase__					,       dtype=np.floataa	) for array in input_values]
         elif (
             not isinstance(lowerCAmelCase__					,       np.ndarray	)
             and isinstance(input_values[0]					,       np.ndarray	)
             and input_values[0].dtype is np.dtype(np.floataa	)
         ):
              __A					:					str								=		[array.astype(np.floataa	) for array in input_values]
         elif isinstance(lowerCAmelCase__					,       np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
              __A					:					Any								=		input_values.astype(np.floataa	)

         # convert attention_mask to correct format
         __A					:					Tuple								=		padded_inputs.get('attention_mask'	)
         if attention_mask is not None:
              __A					:					str								=		[np.asarray(lowerCAmelCase__					,       dtype=np.intaa	) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
              __A					:					Tuple								=		(
                  attention_mask
                  if self._get_padding_strategies(lowerCAmelCase__					,       max_length=lowerCAmelCase__	) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              __A					:					Dict								=		self.zero_mean_unit_var_norm(
                  padded_inputs['input_values']					,       attention_mask=lowerCAmelCase__					,       padding_value=self.padding_value	)

         if return_tensors is not None:
              __A					:					Optional[Any]								=		padded_inputs.convert_to_tensors(lowerCAmelCase__	)

         return padded_inputs










"""simple docstring"""




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_attn_layer.in_proj_weight.chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       pt_attn_layer.in_proj_bias.chunk(3			,		dim=0  )

						_UpperCamelCase     :   int      =       pt_attn_layer.out_proj.weight
						_UpperCamelCase     :   List[Any]      =       pt_attn_layer.out_proj.bias

						_UpperCamelCase     :   Any      =       q_proj
						_UpperCamelCase     :   str      =       q_proj_bias

						_UpperCamelCase     :   str      =       k_proj
						_UpperCamelCase     :   List[str]      =       k_proj_bias

						_UpperCamelCase     :   Dict      =       v_proj
						_UpperCamelCase     :   Dict      =       v_proj_bias

						_UpperCamelCase     :   Dict      =       out_proj_weights
						_UpperCamelCase     :   Optional[Any]      =       out_proj_bias





def 			a_							(				_lowercase			,		_lowercase  ):
						copy_linear(hf_mlp.fca			,		pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca			,		pt_mlp.c_proj  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       pt_linear.weight
						_UpperCamelCase     :   List[str]      =       pt_linear.bias





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy layer norms
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )

						# copy MLP
						copy_mlp(hf_layer.mlp			,		pt_layer.mlp  )

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,		pt_layer.attn  )





def 			a_							(				_lowercase			,		_lowercase  ):
						for hf_layer, pt_layer in zip(_lowercase			,		_lowercase  ):
												copy_layer(_lowercase			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy  embeds
						_UpperCamelCase     :   Tuple      =       pt_model.token_embedding.weight
						_UpperCamelCase     :   List[Any]      =       pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,		pt_model.ln_final  )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,		pt_model.transformer.resblocks  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   str      =       pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   Tuple      =       pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,		pt_model.visual.ln_pre  )
						copy_linear(hf_model.vision_model.post_layernorm			,		pt_model.visual.ln_post  )

						# copy embeds
						_UpperCamelCase     :   str      =       pt_model.visual.conva.weight.data
						_UpperCamelCase     :   Dict      =       pt_model.visual.class_embedding
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,		pt_model.visual.transformer.resblocks  )





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None  ):
						if config_path is not None:
												_UpperCamelCase     :   List[Any]      =       CLIPConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   Optional[int]      =       CLIPConfig(projection_dim=512			,		text_config={}			,		vision_config={}  )

						_UpperCamelCase     :   str      =       CLIPModel(_lowercase  ).eval()

						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       load(_lowercase			,		device='''cpu'''			,		jit=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.eval()

						copy_text_model_and_projection(_lowercase			,		_lowercase  )
						copy_vison_model_and_projection(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   int      =       pt_model.logit_scale

						_UpperCamelCase     :   List[Any]      =       torch.arange(0			,		77  ).unsqueeze(0  )
						_UpperCamelCase     :   Optional[int]      =       torch.randn(1			,		3			,		224			,		224  )

						_UpperCamelCase     :   Tuple      =       hf_model(input_ids=_lowercase			,		pixel_values=_lowercase			,		return_dict=_lowercase  )
						_UpperCamelCase     :   Dict      =       hf_outputs.logits_per_image
						_UpperCamelCase     :   Any      =       hf_outputs.logits_per_text
						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_model(_lowercase			,		_lowercase  )

						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )
						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )

						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						UpperCamelCase_	 =parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



