import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Any 			=    pt_attn_layer.in_proj_weight.chunk(3		,							dim=0   )
    __snake_case       :				Dict 			=    pt_attn_layer.in_proj_bias.chunk(3		,							dim=0   )

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

    __snake_case       :				Any 			=    q_proj
    __snake_case       :				str 			=    q_proj_bias

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

    __snake_case       :				Dict 			=    v_proj
    __snake_case       :				Dict 			=    v_proj_bias

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					List[Any]   ):







    '''simple docstring'''
    copy_linear(hf_mlp.fca		,							pt_mlp.c_fc   )
    copy_linear(hf_mlp.fca		,							pt_mlp.c_proj   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					Tuple   ):







    '''simple docstring'''
    __snake_case       :				str 			=    pt_linear.weight
    __snake_case       :				List[str] 			=    pt_linear.bias







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					Tuple   ):







    '''simple docstring'''
    # 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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[int]		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]   ):







    '''simple docstring'''
    for hf_layer, pt_layer in zip(_lowercase		,							_lowercase   ):
        copy_layer(_lowercase		,							_lowercase   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					List[Any]   ):







    '''simple docstring'''
    # copy  embeds
    __snake_case       :				Tuple 			=    pt_model.token_embedding.weight
    __snake_case       :				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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					List[str]   ):







    '''simple docstring'''
    # copy projection
    __snake_case       :				str 			=    pt_model.text_projection.data.T

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[str]		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    # copy projection
    __snake_case       :				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
    __snake_case       :				str 			=    pt_model.visual.conva.weight.data
    __snake_case       :				Dict 			=    pt_model.visual.class_embedding
    __snake_case       :				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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict		,							__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					int=None   ):







    '''simple docstring'''
    if config_path is not None:
        __snake_case       :				List[Any] 			=    CLIPConfig.from_pretrained(_lowercase   )
    else:
        __snake_case       :				Optional[int] 			=    CLIPConfig(projection_dim=5_1_2		,							text_config={}		,							vision_config={}   )

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

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

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

    __snake_case       :				List[Any] 			=    torch.arange(0		,							7_7   ).unsqueeze(0   )
    __snake_case       :				Optional[int] 			=    torch.randn(1		,							3		,							2_2_4		,							2_2_4   )

    __snake_case       :				Tuple 			=    hf_model(input_ids=_lowercase		,							pixel_values=_lowercase		,							return_dict=_lowercase   )
    __snake_case       :				Dict 			=    hf_outputs.logits_per_image
    __snake_case       :				Any 			=    hf_outputs.logits_per_text
    __snake_case       :				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__":
     lowercase_  							=			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")
     lowercase_  							=			parser.parse_args()

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










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






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




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

										with open(self.feature_extraction_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					return_tensors='''np'''				)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2				)






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],					encoded_processor[key]				)






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										self.assertListEqual(
										    processor.model_input_names,					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)



