
'''simple docstring'''




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowercase			:				List[str]    		=			get_tests_dir('fixtures')





class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						mock.Mock()
         A       :   Dict					=						500
         A       :   List[Any]					=						{}
         A       :   Optional[Any]					=						HTTPError
         A       :   Optional[Any]					=						{}

         # Download this model to make sure it's in the cache.
         A       :   List[str]					=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''	)
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch('''requests.Session.request'''						,			return_value=SCREAMING_SNAKE_CASE	) as mock_head:
             A       :   Optional[Any]					=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''	)
             # This check we did call the fake head request
             mock_head.assert_called()





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						ViTImageProcessor.from_pretrained(
             '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''	)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE	):
             # config is in subfolder, the following should not work without specifying the subfolder
             A       :   Tuple					=						AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''	)

         A       :   List[Any]					=						AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/stable-diffusion-all-variants'''						,			subfolder='''feature_extractor'''	)

         self.assertIsNotNone(SCREAMING_SNAKE_CASE	)





@is_staging_test
class  A				(							unittest.TestCase						):





     @classmethod
     def 				__lowerCAmelCase							(							cls	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						TOKEN
         HfFolder.save_token(SCREAMING_SNAKE_CASE	)





     @classmethod
     def 				__lowerCAmelCase							(							cls	) ->				Union[str, Any]:






         """simple docstring"""






         try:
             delete_repo(token=cls._token						,			repo_id='''test-image-processor'''	)
         except HTTPError:
             pass

         try:
             delete_repo(token=cls._token						,			repo_id='''valid_org/test-image-processor-org'''	)
         except HTTPError:
             pass

         try:
             delete_repo(token=cls._token						,			repo_id='''test-dynamic-image-processor'''	)
         except HTTPError:
             pass





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   List[str]					=						ViTImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)
         image_processor.push_to_hub('''test-image-processor'''						,			use_auth_token=self._token	)

         A       :   Dict					=						ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor'	)
         for k, v in image_processor.__dict__.items():
             self.assertEqual(SCREAMING_SNAKE_CASE						,			getattr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)

         # Reset repo
         delete_repo(token=self._token						,			repo_id='''test-image-processor'''	)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 SCREAMING_SNAKE_CASE						,			repo_id='''test-image-processor'''						,			push_to_hub=SCREAMING_SNAKE_CASE						,			use_auth_token=self._token	)

         A       :   Tuple					=						ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor'	)
         for k, v in image_processor.__dict__.items():
             self.assertEqual(SCREAMING_SNAKE_CASE						,			getattr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Tuple					=						ViTImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)
         image_processor.push_to_hub('''valid_org/test-image-processor'''						,			use_auth_token=self._token	)

         A       :   Union[str, Any]					=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''	)
         for k, v in image_processor.__dict__.items():
             self.assertEqual(SCREAMING_SNAKE_CASE						,			getattr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)

         # Reset repo
         delete_repo(token=self._token						,			repo_id='''valid_org/test-image-processor'''	)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 SCREAMING_SNAKE_CASE						,			repo_id='''valid_org/test-image-processor-org'''						,			push_to_hub=SCREAMING_SNAKE_CASE						,			use_auth_token=self._token	)

         A       :   Dict					=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''	)
         for k, v in image_processor.__dict__.items():
             self.assertEqual(SCREAMING_SNAKE_CASE						,			getattr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         CustomImageProcessor.register_for_auto_class()
         A       :   List[Any]					=						CustomImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

         image_processor.push_to_hub('''test-dynamic-image-processor'''						,			use_auth_token=self._token	)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             image_processor.auto_map						,			{'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}						,			)

         A       :   Optional[int]					=						AutoImageProcessor.from_pretrained(
             F'{USER}/test-dynamic-image-processor'						,			trust_remote_code=SCREAMING_SNAKE_CASE	)
         # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
         self.assertEqual(new_image_processor.__class__.__name__						,			'''CustomImageProcessor'''	)






'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase			:				Union[str, Any]    		=			{
    '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',
}

lowercase			:				Tuple    		=			AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   Tuple					=						create_model(
        '''HTSAT-tiny'''		,				'''roberta'''		,				snake_case__		,				precision='''fp32'''		,				device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,				enable_fusion=snake_case__		,				fusion_type='''aff_2d''' if enable_fusion else None		,				)
    return model, model_cfg







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{}

    A       :   str					=						R'''.*sequential.(\d+).*'''
    A       :   Union[str, Any]					=						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:
                A       :   Any					=						key.replace(snake_case__		,				snake_case__						)

        if re.match(snake_case__		,				snake_case__						):
            # replace sequential layers with list
            A       :   Any					=						re.match(snake_case__		,				snake_case__						).group(1						)

            A       :   List[str]					=						key.replace(F'sequential.{sequential_layer}.'		,				F'layers.{int(snake_case__						)//3}.linear.'						)
        elif re.match(snake_case__		,				snake_case__						):
            A       :   Union[str, Any]					=						int(re.match(snake_case__		,				snake_case__						).group(1						)						)

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

            A       :   Optional[Any]					=						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
            A       :   int					=						value
            A       :   List[Any]					=						mixed_qkv.size(0						) // 3

            A       :   Union[str, Any]					=						mixed_qkv[:qkv_dim]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim : qkv_dim * 2]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim * 2 :]

            A       :   Tuple					=						query_layer
            A       :   Union[str, Any]					=						key_layer
            A       :   Optional[int]					=						value_layer
        else:
            A       :   Dict					=						value

    return model_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   int					=						init_clap(snake_case__		,				enable_fusion=snake_case__						)

    clap_model.eval()
    A       :   str					=						clap_model.state_dict()
    A       :   Union[str, Any]					=						rename_state_dict(snake_case__						)

    A       :   Tuple					=						ClapConfig()
    A       :   str					=						enable_fusion
    A       :   str					=						ClapModel(snake_case__						)

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

    model.save_pretrained(snake_case__						)
    transformers_config.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			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')
      lowercase			:				Tuple    		=			parser.parse_args()

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




