import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:str        =		0







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Dict        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Union[str, Any]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Ensure we can load the image processor from the feature extractor config
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:str        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Union[str, Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		CLIPConfig()

            # Create a dummy config file with image_proceesor_type
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Optional[Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            # remove image_processor_type to make sure config.json alone is enough to load image processor locally
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''image_processor_type''')
            __UpperCamelCase					:List[str]        =		CLIPImageProcessor(**__lowercase)

            # save in new folder
            model_config.save_pretrained(__lowercase)
            config.save_pretrained(__lowercase)

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase					:Union[str, Any]        =		json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)

            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with self.assertRaisesRegex(
            __lowercase			,			'''clip-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''clip-base''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        with self.assertRaisesRegex(
            __lowercase			,			r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained(__lowercase			,			revision='''aaaaaa''')







    def       UpperCamelCase__       (     self)	->     List[str]:
        with self.assertRaisesRegex(
            __lowercase			,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,			):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')







    def       UpperCamelCase__       (     self)	->     str:
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
        self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')

        # Test image processor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(__lowercase)
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(__lowercase			,			trust_remote_code=__lowercase)
        self.assertEqual(reloaded_image_processor.__class__.__name__			,			'''NewImageProcessor''')







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(__lowercase):
                AutoImageProcessor.register(__lowercase			,			__lowercase)

            with tempfile.TemporaryDirectory() as tmpdirname:
                __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
                __UpperCamelCase					:List[str]        =		Path(__lowercase) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
                json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

                __UpperCamelCase					:int        =		CustomImageProcessor.from_pretrained(__lowercase)

            # Now that the config is registered, it can be used as any other config with the auto-API
            with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(__lowercase)
                __UpperCamelCase					:int        =		AutoImageProcessor.from_pretrained(__lowercase)
                self.assertIsInstance(__lowercase			,			__lowercase)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







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


        class      lowerCamelCase_					(       UpperCAmelCase_			):






            '''simple docstring'''






            a__     :						List[str]      =				True

        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote code is disabled, we load the local one.
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote is enabled, we load from the Hub
            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(not hasattr(__lowercase			,			'''is_local'''))

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowercase										= logging.get_logger(__name__)
logging.set_verbosity_info()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''self_attn''': '''ngram_self_attn''',
        '''cross_attn''': '''encoder_attn''',
        '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
        '''feed_forward_layer_norm''': '''final_layer_norm''',
        '''feed_forward''': '''''',
        '''intermediate''': '''fc1''',
        '''output''': '''fc2''',
        '''key_proj''': '''k_proj''',
        '''query_proj''': '''q_proj''',
        '''value_proj''': '''v_proj''',
        '''word_embeddings''': '''embed_tokens''',
        '''embeddings_layer_norm''': '''emb_layer_norm''',
        '''relative_pos_embeddings''': '''relative_linear''',
        '''ngram_embeddings''': '''ngram_input_embed''',
        '''position_embeddings''': '''embed_positions''',
    }

    for key in loading_info["missing_keys"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

        __UpperCamelCase					:Optional[Any]        =		False
        for attribute in attributes:
            if attribute in mapping:
                __UpperCamelCase					:str        =		mapping[attribute]
                if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ) and len(SCREAMING_SNAKE_CASE     ) > 0:
                    __UpperCamelCase					:Optional[int]        =		attribute
            elif hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                __UpperCamelCase					:List[Any]        =		attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                __UpperCamelCase					:Tuple        =		old_model.weight
                logger.info(f"""{attribute} is initialized."""     )
                __UpperCamelCase					:Union[str, Any]        =		True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                __UpperCamelCase					:Union[str, Any]        =		old_model.bias
                logger.info(f"""{attribute} is initialized"""     )
                __UpperCamelCase					:List[Any]        =		True
                break
            elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE  ,      '''in_proj_weight'''     ):
                __UpperCamelCase					:str        =		old_model.in_proj_weight.shape[0] // 3
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.in_proj_weight[:embed_dim, :]     )
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_bias[:embed_dim]     )

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        if not is_key_init:
            raise ValueError(f"""{key} was not correctly initialized!"""     )

    print(f"""Saving model to {pytorch_dump_folder_path}"""     )
    prophet.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





