
'''simple docstring'''




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Dict					=						'''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
    A       :   str					=						Image.open(requests.get(snake_case__		,				stream=snake_case__						).raw						).convert('''RGB'''						)

    return image







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						[]
    # fmt: off

    # vision encoder
    rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')						)
    rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')						)
    rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')						)
    rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')						)
    rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')						)
    rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')						)

    for i in range(config.vision_config.num_hidden_layers						):
        rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)						)
        rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')						)
        rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')						)

    # QFormer
    rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''')						)
    rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''')						)

    # fmt: on
    return rename_keys







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						dct.pop(snake_case__						)
    A       :   Union[str, Any]					=						val







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    for i in range(config.vision_config.num_hidden_layers						):
        # read in original q and v biases
        A       :   Optional[Any]					=						state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'						)
        A       :   Union[str, Any]					=						state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'						)

        # next, set bias in the state dict
        A       :   Dict					=						torch.cat((q_bias, torch.zeros_like(snake_case__		,				requires_grad=snake_case__						), v_bias)						)
        A       :   str					=						qkv_bias







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   int					=						364 if '''coco''' in model_name else 224
    A       :   List[str]					=						InstructBlipVisionConfig(image_size=snake_case__						).to_dict()

    # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
    # seems like flan-T5 models don't have bos_token_id properly set?
    if "t5-xl" in model_name:
        A       :   int					=						TaConfig.from_pretrained('''google/flan-t5-xl'''		,				dense_act_fn='''gelu'''		,				bos_token_id=1						).to_dict()
    elif "t5-xxl" in model_name:
        A       :   Optional[Any]					=						TaConfig.from_pretrained('''google/flan-t5-xxl'''		,				dense_act_fn='''gelu'''		,				bos_token_id=1						).to_dict()
    elif "vicuna-7b" in model_name:
        A       :   str					=						LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''		,				vocab_size=3_2001						).to_dict()
    elif "vicuna-13b" in model_name:
        A       :   Tuple					=						LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''		,				vocab_size=3_2001						).to_dict()
    else:
        raise ValueError('''Model name not supported'''						)

    # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
    A       :   int					=						InstructBlipQFormerConfig(vocab_size=3_0523						).to_dict()
    A       :   Optional[int]					=						InstructBlipConfig(vision_config=snake_case__		,				text_config=snake_case__		,				qformer_config=snake_case__						)

    return config, image_size







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=None		,				snake_case__=False						):




    '''simple docstring'''



    A       :   Optional[int]					=						AutoTokenizer.from_pretrained('''bert-base-uncased'''		,				truncation_side='''left'''						)
    qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}						)

    if "t5" in model_name:
        A       :   Dict					=						TaTokenizerFast.from_pretrained('''google/flan-t5-xl'''		,				truncation_side='''left'''						)
    elif "vicuna" in model_name:
        # the following was used in the original implementation:
        # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
        # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
        # tokenizer.add_special_tokens({"bos_token": "</s>"})
        # tokenizer.add_special_tokens({"eos_token": "</s>"})
        # tokenizer.add_special_tokens({"unk_token": "</s>"})
        A       :   Optional[int]					=						LlamaTokenizerFast.from_pretrained(
            '''huggyllama/llama-7b'''		,				truncation_side='''left'''		,				bos_token='''</s>'''		,				unk_token='''</s>'''						)
        tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}						)

    A,  A       :   Optional[Any]					=						get_blipa_config(snake_case__						)
    A       :   List[str]					=						InstructBlipForConditionalGeneration(snake_case__						).eval()

    A       :   str					=						{
        '''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
        '''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
        '''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
        '''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
    }

    A,  A       :   List[str]					=						model_name_to_original[model_name]

    # load original model
    print('''Loading original model...'''						)
    A       :   List[Any]					=						'''cuda:1''' if torch.cuda.is_available() else '''cpu'''
    A       :   int					=						'''cuda:2''' if torch.cuda.is_available() else '''cpu'''
    A,  A,  A       :   List[str]					=						load_model_and_preprocess(
        name=snake_case__		,				model_type=snake_case__		,				is_eval=snake_case__		,				device=snake_case__						)
    original_model.eval()
    print('''Done!'''						)

    # update state dict keys
    A       :   Tuple					=						original_model.state_dict()
    A       :   Tuple					=						create_rename_keys(snake_case__						)
    for src, dest in rename_keys:
        rename_key(snake_case__		,				snake_case__		,				snake_case__						)

    # some keys can be renamed efficiently
    for key, val in state_dict.copy().items():
        A       :   int					=						state_dict.pop(snake_case__						)
        if key.startswith('''Qformer.bert'''						):
            A       :   List[Any]					=						key.replace('''Qformer.bert'''		,				'''qformer'''						)
        if "attention.self" in key:
            A       :   Optional[Any]					=						key.replace('''self'''		,				'''attention'''						)
        if "llm_proj" in key:
            A       :   Union[str, Any]					=						key.replace('''llm_proj'''		,				'''language_projection'''						)
        if "t5_proj" in key:
            A       :   int					=						key.replace('''t5_proj'''		,				'''language_projection'''						)
        if key.startswith('''llm_model'''						):
            A       :   Any					=						key.replace('''llm_model'''		,				'''language_model'''						)
        if key.startswith('''t5'''						):
            A       :   int					=						key.replace('''t5'''		,				'''language'''						)
        A       :   Any					=						val

    # read in qv biases
    read_in_q_v_bias(snake_case__		,				snake_case__						)

    # note: weights get loaded in torch.float32 by default
    hf_model.load_state_dict(snake_case__		,				strict=snake_case__						)

    A       :   Optional[Any]					=						load_demo_image()
    A       :   int					=						'''What is unusual about this image?'''

    # create processor
    A       :   Dict					=						BlipImageProcessor(
        size={'''height''': image_size, '''width''': image_size}		,				image_mean=snake_case__		,				image_std=snake_case__						)
    A       :   Union[str, Any]					=						InstructBlipProcessor(
        image_processor=snake_case__		,				tokenizer=snake_case__		,				qformer_tokenizer=snake_case__		,				)
    A       :   str					=						processor(images=snake_case__		,				text=snake_case__		,				return_tensors='''pt'''						).to(snake_case__						)

    # make sure processor creates exact same pixel values
    A       :   List[str]					=						vis_processors['''eval'''](snake_case__						).unsqueeze(0						).to(snake_case__						)
    A       :   List[str]					=						inputs.pixel_values
    assert torch.allclose(original_pixel_values.to(pixel_values.device						)		,				snake_case__						)

    original_model.to(snake_case__						)
    hf_model.to(snake_case__						)
    with torch.no_grad():
        if "vicuna" in model_name:
            A       :   Tuple					=						original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}						).logits
            A       :   List[Any]					=						hf_model(**snake_case__						).logits
        else:
            A       :   List[Any]					=						original_model(
                {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}						).logits
            A       :   Tuple					=						tokenizer('''\n'''		,				return_tensors='''pt'''						).input_ids.to(snake_case__						)
            A       :   Union[str, Any]					=						label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,				-100						)
            A       :   List[str]					=						hf_model(**snake_case__		,				labels=snake_case__						).logits

    print('''First values of original logits:'''		,				original_logits[0, :3, :3]						)
    print('''First values of HF logits:'''		,				logits[0, :3, :3]						)

    # assert values
    assert original_logits.shape == logits.shape
    A       :   Optional[Any]					=						1E-4 if '''vicuna''' in model_name else 1E-5
    assert torch.allclose(original_logits.to(logits.device						)		,				snake_case__		,				atol=snake_case__						)
    print('''Looks ok!'''						)

    print('''Generating with original model...'''						)
    A       :   Any					=						original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt}		,				num_beams=5						)

    # important: we need to cast the weights of the HF model to the appropriate type
    print('''Generating with HF model...'''						)
    A       :   str					=						hf_model.generate(
        **snake_case__		,				do_sample=snake_case__		,				num_beams=5		,				max_length=256		,				min_length=1		,				top_p=0.9		,				repetition_penalty=1.5		,				length_penalty=1.0		,				temperature=1		,				)
    if "vicuna" in model_name:
        # convert output id 0 to 2 (eos_token_id)
        # TODO add this in the generate method?
        A       :   Optional[int]					=						2
    print('''Original generation:'''		,				snake_case__						)
    A       :   int					=						processor.batch_decode(snake_case__		,				skip_special_tokens=snake_case__						)
    A       :   Optional[Any]					=						[text.strip() for text in output_text]
    print('''HF generation:'''		,				snake_case__						)

    if pytorch_dump_folder_path is not None:
        processor.save_pretrained(snake_case__						)
        hf_model.save_pretrained(snake_case__						)

    if push_to_hub:
        processor.push_to_hub(F'Salesforce/{model_name}'						)
        hf_model.push_to_hub(F'Salesforce/{model_name}'						)


if __name__ == "__main__":
      lowercase			:				List[Any]    		=			argparse.ArgumentParser()
      lowercase			:				Dict    		=			[
          'instructblip-vicuna-7b',
          'instructblip-vicuna-13b',
          'instructblip-flan-t5-xl',
          'instructblip-flan-t5-xxl',
      ]
      parser.add_argument(
          '--model_name',
          default='instructblip-flan-t5-xl',
          choices=choices,
          type=str,
          help='Path to hf config.json of model to convert',
      )
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether to push the model and processor to the hub after converting',
      )

      lowercase			:				Union[str, Any]    		=			parser.parse_args()

      convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
 # Initialising the CNN
 # (Sequential- Building the model layer by layer)
 lowerCamelCase							:   Any  						=	models.Sequential()

 # Step 1 - Convolution
 # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
 # (3,3) is the kernel size (filter matrix)
 classifier.add(
     layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
 )

 # Step 2 - Pooling
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Step 3 - Flattening
 classifier.add(layers.Flatten())

 # Step 4 - Full connection
 classifier.add(layers.Dense(units=128, activation='relu'))
 classifier.add(layers.Dense(units=1, activation='sigmoid'))

 # Compiling the CNN
 classifier.compile(
     optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
 )

 # Part 2 - Fitting the CNN to the images

 # Load Trained model weights

 # from keras.models import load_model
 # regressor=load_model('cnn.h5')

 lowerCamelCase							:   Optional[Any]  						=	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 lowerCamelCase							:   List[str]  						=	test_datagen.flow_from_directory(
     'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
 )

 classifier.save('cnn.h5')

 # Part 3 - Making new predictions

 lowerCamelCase							:   List[str]  						=	tf.keras.preprocessing.image.load_img(
     'dataset/single_prediction/image.png', target_size=(64, 64)
 )
 lowerCamelCase							:   Optional[int]  						=	tf.keras.preprocessing.image.img_to_array(test_image)
 lowerCamelCase							:   str  						=	np.expand_dims(test_image, axis=0)
 lowerCamelCase							:   List[str]  						=	classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  lowerCamelCase							:   Any  						=	'Normal'
 if result[0][0] == 1:
  lowerCamelCase							:   Any  						=	'Abnormality detected'




