"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

__UpperCamelCase										=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def    UpperCAmelCase       (						UpperCAmelCase   )							-> str:
     for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
               snake_case_   			=   model_type_to_module_name(UpperCAmelCase   )

               snake_case_   			=   importlib.import_module(f'.{module_name}'		,   'transformers.models'   )
               try:
                    return getattr(UpperCAmelCase		,   UpperCAmelCase   )
               except AttributeError:
                    continue

     for processor in PROCESSOR_MAPPING._extra_content.values():
          if getattr(UpperCAmelCase		,   '__name__'		,   UpperCAmelCase   ) == class_name:
               return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
     snake_case_   			=   importlib.import_module('transformers'   )
     if hasattr(UpperCAmelCase		,   UpperCAmelCase   ):
          return getattr(UpperCAmelCase		,   UpperCAmelCase   )

     return None




class 					UpperCamelCase   :







       def __init__(					self) -> str:
            raise EnvironmentError(
                'AutoProcessor is designed to be instantiated '
                'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.')







       @classmethod
       @replace_list_option_in_docstrings(lowerCAmelCase__)
       def 						a_		(					cls,				lowerCAmelCase__,				**lowerCAmelCase__) -> Dict:
            snake_case_   			=   kwargs.pop('config',				lowerCAmelCase__)
            snake_case_   			=   kwargs.pop('trust_remote_code',				lowerCAmelCase__)
            snake_case_   			=   True

            snake_case_   			=   None
            snake_case_   			=   None

            # First, let's see if we have a preprocessor config.
            # Filter the kwargs for `get_file_from_repo`.
            snake_case_   			=   {
                key: kwargs[key] for key in inspect.signature(lowerCAmelCase__).parameters.keys() if key in kwargs
            }
            # Let's start by checking whether the processor class is saved in an image processor
            snake_case_   			=   get_file_from_repo(
                lowerCAmelCase__,				lowerCAmelCase__,				**lowerCAmelCase__)
            if preprocessor_config_file is not None:
                 snake_case_			,      snake_case_   			=   ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__,				**lowerCAmelCase__)
                 snake_case_   			=   config_dict.get('processor_class',				lowerCAmelCase__)
                 if "AutoProcessor" in config_dict.get('auto_map',				{}):
                      snake_case_   			=   config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
            if preprocessor_config_file is not None and processor_class is None:
                 snake_case_			,      snake_case_   			=   FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase__,				**lowerCAmelCase__)
                 snake_case_   			=   config_dict.get('processor_class',				lowerCAmelCase__)
                 if "AutoProcessor" in config_dict.get('auto_map',				{}):
                      snake_case_   			=   config_dict['auto_map']['AutoProcessor']

            if processor_class is None:
                 # Next, let's check whether the processor class is saved in a tokenizer
                 snake_case_   			=   get_file_from_repo(
                     lowerCAmelCase__,				lowerCAmelCase__,				**lowerCAmelCase__)
                 if tokenizer_config_file is not None:
                      with open(lowerCAmelCase__,				encoding='utf-8') as reader:
                           snake_case_   			=   json.load(lowerCAmelCase__)

                      snake_case_   			=   config_dict.get('processor_class',				lowerCAmelCase__)
                      if "AutoProcessor" in config_dict.get('auto_map',				{}):
                           snake_case_   			=   config_dict['auto_map']['AutoProcessor']

            if processor_class is None:
                 # Otherwise, load config, if it can be loaded.
                 if not isinstance(lowerCAmelCase__,				lowerCAmelCase__):
                      snake_case_   			=   AutoConfig.from_pretrained(
                          lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)

                 # And check if the config contains the processor class.
                 snake_case_   			=   getattr(lowerCAmelCase__,				'processor_class',				lowerCAmelCase__)
                 if hasattr(lowerCAmelCase__,				'auto_map') and "AutoProcessor" in config.auto_map:
                      snake_case_   			=   config.auto_map['AutoProcessor']

            if processor_class is not None:
                 snake_case_   			=   processor_class_from_name(lowerCAmelCase__)

            snake_case_   			=   processor_auto_map is not None
            snake_case_   			=   processor_class is not None or type(lowerCAmelCase__) in PROCESSOR_MAPPING
            snake_case_   			=   resolve_trust_remote_code(
                lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)

            if has_remote_code and trust_remote_code:
                 snake_case_   			=   get_class_from_dynamic_module(
                     lowerCAmelCase__,				lowerCAmelCase__,				**lowerCAmelCase__)
                 snake_case_   			=   kwargs.pop('code_revision',				lowerCAmelCase__)
                 if os.path.isdir(lowerCAmelCase__):
                      processor_class.register_for_auto_class()
                 return processor_class.from_pretrained(
                     lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)
            elif processor_class is not None:
                 return processor_class.from_pretrained(
                     lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)
            # Last try: we use the PROCESSOR_MAPPING.
            elif type(lowerCAmelCase__) in PROCESSOR_MAPPING:
                 return PROCESSOR_MAPPING[type(lowerCAmelCase__)].from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__)

            # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
            # tokenizer.
            try:
                 return AutoTokenizer.from_pretrained(
                     lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)
            except Exception:
                 try:
                      return AutoImageProcessor.from_pretrained(
                          lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)
                 except Exception:
                      pass

                 try:
                      return AutoFeatureExtractor.from_pretrained(
                          lowerCAmelCase__,				trust_remote_code=lowerCAmelCase__,				**lowerCAmelCase__)
                 except Exception:
                      pass

            raise ValueError(
                f'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
                'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
                'the files of at least one of those processing classes.')







       @staticmethod
       def 						a_		(					lowerCAmelCase__,				lowerCAmelCase__) -> List[Any]:
            PROCESSOR_MAPPING.register(lowerCAmelCase__,				lowerCAmelCase__)


"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # 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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

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







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   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_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   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    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )
     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )

     hf_model.save_pretrained(UpperCAmelCase   )


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)

