
"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__magic_name__       =			{
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

__magic_name__       =			AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = create_model(
		    """HTSAT-tiny"""  ,				"""roberta"""  ,				UpperCamelCase_  ,				precision="""fp32"""  ,				device="""cuda:0""" if torch.cuda.is_available() else """cpu"""  ,				enable_fusion=UpperCamelCase_  ,				fusion_type="""aff_2d""" if enable_fusion else None  ,				)
		return model, model_cfg



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = {}

		__SCREAMING_SNAKE_CASE            = r""".*sequential.(\d+).*"""
		__SCREAMING_SNAKE_CASE            = r""".*_projection.(\d+).*"""

		for key, value in state_dict.items():
				# check if any key needs to be modified
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								__SCREAMING_SNAKE_CASE            = key.replace(UpperCamelCase_  ,				UpperCamelCase_				)

				if re.match(UpperCamelCase_  ,				UpperCamelCase_				):
						# replace sequential layers with list
						__SCREAMING_SNAKE_CASE            = re.match(UpperCamelCase_  ,				UpperCamelCase_				).group(1				)

						__SCREAMING_SNAKE_CASE            = key.replace(f"sequential.{sequential_layer}."  ,				f"layers.{int(UpperCamelCase_				)//3}.linear."				)
				elif re.match(UpperCamelCase_  ,				UpperCamelCase_				):
						__SCREAMING_SNAKE_CASE            = int(re.match(UpperCamelCase_  ,				UpperCamelCase_				).group(1				)				)

						# Because in CLAP they use `nn.Sequential`...
						__SCREAMING_SNAKE_CASE            = 1 if projecton_layer == 0 else 2

						__SCREAMING_SNAKE_CASE            = key.replace(f"_projection.{projecton_layer}."  ,				f"_projection.linear{transformers_projection_layer}."				)

				if "audio" and "qkv" in key:
						# split qkv into query key and value
						__SCREAMING_SNAKE_CASE            = value
						__SCREAMING_SNAKE_CASE            = mixed_qkv.size(0				) // 3

						__SCREAMING_SNAKE_CASE            = mixed_qkv[:qkv_dim]
						__SCREAMING_SNAKE_CASE            = mixed_qkv[qkv_dim : qkv_dim * 2]
						__SCREAMING_SNAKE_CASE            = mixed_qkv[qkv_dim * 2 :]

						__SCREAMING_SNAKE_CASE            = query_layer
						__SCREAMING_SNAKE_CASE            = key_layer
						__SCREAMING_SNAKE_CASE            = value_layer
				else:
						__SCREAMING_SNAKE_CASE            = value

		return model_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = init_clap(UpperCamelCase_  ,				enable_fusion=UpperCamelCase_				)

		clap_model.eval()
		__SCREAMING_SNAKE_CASE            = clap_model.state_dict()
		__SCREAMING_SNAKE_CASE            = rename_state_dict(UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = ClapConfig()
		__SCREAMING_SNAKE_CASE            = enable_fusion
		__SCREAMING_SNAKE_CASE            = ClapModel(UpperCamelCase_				)

		# ignore the spectrogram embedding layer
		model.load_state_dict(UpperCamelCase_  ,				strict=UpperCamelCase_				)

		model.save_pretrained(UpperCamelCase_				)
		transformers_config.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
							__magic_name__       =			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")
							parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
							__magic_name__       =			parser.parse_args()

							convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 			__a  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
     '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
     '''It returns the most likely label in the list of provided `labels` for the input text.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      if self.entailment_id == -1:
           raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."						)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]