import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_			):






    '''simple docstring'''






    @register_to_config
    def __init__(     self			,			*,
        __lowercase = 4			,			__lowercase = 768			,			__lowercase			,			__lowercase			,			)	->     str:
        super().__init__()

        __UpperCamelCase					:Any        =		nn.Parameter(torch.zeros(__lowercase))

        # parameters for additional clip time embeddings
        __UpperCamelCase					:Optional[Any]        =		nn.Linear(__lowercase			,			__lowercase)
        __UpperCamelCase					:int        =		nn.Linear(__lowercase			,			__lowercase)

        # parameters for encoder hidden states
        __UpperCamelCase					:Tuple        =		clip_extra_context_tokens
        __UpperCamelCase					:int        =		nn.Linear(
            __lowercase			,			self.clip_extra_context_tokens * cross_attention_dim)
        __UpperCamelCase					:Union[str, Any]        =		nn.Linear(__lowercase			,			__lowercase)
        __UpperCamelCase					:int        =		nn.LayerNorm(__lowercase)







    def       UpperCamelCase__       (     self			,			*, __lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     Tuple:
        if do_classifier_free_guidance:
            # Add the classifier free guidance embeddings to the image embeddings
            __UpperCamelCase					:str        =		image_embeddings.shape[0]
            __UpperCamelCase					:int        =		self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
            __UpperCamelCase					:Any        =		classifier_free_guidance_embeddings.expand(
                __lowercase			,			-1)
            __UpperCamelCase					:List[Any]        =		torch.cat([classifier_free_guidance_embeddings, image_embeddings]			,			dim=0)

        # The image embeddings batch size and the text embeddings batch size are equal
        assert image_embeddings.shape[0] == prompt_embeds.shape[0]

        __UpperCamelCase					:Tuple        =		prompt_embeds.shape[0]

        # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
        # adding CLIP embeddings to the existing timestep embedding, ...
        __UpperCamelCase					:Dict        =		self.embedding_proj(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		self.clip_image_embeddings_project_to_time_embeddings(__lowercase)
        __UpperCamelCase					:Any        =		time_projected_image_embeddings + time_projected_prompt_embeds

        # ... and by projecting CLIP embeddings into four
        # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
        __UpperCamelCase					:Tuple        =		self.clip_extra_context_tokens_proj(__lowercase)
        __UpperCamelCase					:Optional[int]        =		clip_extra_context_tokens.reshape(__lowercase			,			-1			,			self.clip_extra_context_tokens)
        __UpperCamelCase					:Dict        =		clip_extra_context_tokens.permute(0			,			2			,			1)

        __UpperCamelCase					:Dict        =		self.encoder_hidden_states_proj(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		self.text_encoder_hidden_states_norm(__lowercase)
        __UpperCamelCase					:str        =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]			,			dim=1)

        return text_encoder_hidden_states, additive_clip_time_embeddings






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]





