

'''simple docstring'''

import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

def 			__UpperCamelCase							(		lowercase__  :					Tuple	):






     '''simple docstring'''





     __lowercase     						=tf.image.resize(lowercase__,   (3_46, 3_46)	)
     __lowercase     						=tf.image.crop_to_bounding_box(lowercase__,   (3_46 - 2_89) // 2,   (3_46 - 2_89) // 2,   2_89,   2_89	)
     return image

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						=EfficientNetConfig.from_pretrained('google/efficientnet-b7'	)
     __lowercase     						=2_89
     __lowercase     						=6_40
     __lowercase     						={'0': 'LABEL_0', '1': 'LABEL_1'}
     __lowercase     						={'LABEL_0': 0, 'LABEL_1': 1}
     __lowercase     						=[]

     __lowercase     						=BertConfig()
     __lowercase     						=AlignConfig.from_text_vision_configs(
         text_config=lowercase__,   vision_config=lowercase__,   projection_dim=6_40	)
     return config

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase     						=Image.open(requests.get(lowercase__,   stream=lowercase__	).raw	)
     return im

def 			__UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase     						=EfficientNetImageProcessor(
         do_center_crop=lowercase__,   rescale_factor=1 / 127.5,   rescale_offset=lowercase__,   do_normalize=lowercase__,   include_top=lowercase__,   resample=Image.BILINEAR,   )
     __lowercase     						=BertTokenizer.from_pretrained('bert-base-uncased'	)
     __lowercase     						=64
     __lowercase     						=AlignProcessor(image_processor=lowercase__,   tokenizer=lowercase__	)
     return processor

def 			__UpperCamelCase							(		lowercase__  :					Tuple	):






     '''simple docstring'''





     __lowercase     						=[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
     __lowercase     						=list(set(lowercase__	)	)
     __lowercase     						=sorted(lowercase__	)
     __lowercase     						=len(lowercase__	)
     __lowercase     						={b: str(lowercase__	) for b, i in zip(lowercase__,   range(lowercase__	)	)}

     __lowercase     						=[]
     rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
     rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
     rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
     rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
     rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

     for b in block_names:
          __lowercase     						=block_name_mapping[b]
          rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')	)
          rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')	)
          rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')	)
          rename_keys.append(
              (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')	)
          rename_keys.append(
              (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')	)
          rename_keys.append(
              (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')	)
          rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')	)
          rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')	)
          rename_keys.append(
              (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')	)
          rename_keys.append(
              (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')	)

          rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')	)
          rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')	)
          rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')	)
          rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')	)
          rename_keys.append(
              (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')	)
          rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')	)
          rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')	)
          rename_keys.append(
              (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')	)
          rename_keys.append(
              (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')	)

     __lowercase     						={}
     for item in rename_keys:
          if item[0] in original_param_names:
               __lowercase     						='vision_model.' + item[1]

    # BERT text encoder
     __lowercase     						=[]
     __lowercase     						='tf_bert_model/bert'
     __lowercase     						='text_model'
     for i in range(12	):
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.query.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.query.bias''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.key.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.key.bias''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.value.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
                  F'''{new}.encoder.layer.{i}.attention.self.value.bias''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
                  F'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
                  F'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
                  F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
                  F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
                  F'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
              )	)
          rename_keys.append(
              (
                  F'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
                  F'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
              )	)
          rename_keys.append(
              (F'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', F'''{new}.encoder.layer.{i}.output.dense.weight''')	)
          rename_keys.append(
              (F'''{old}/encoder/layer_._{i}/output/dense/bias:0''', F'''{new}.encoder.layer.{i}.output.dense.bias''')	)
          rename_keys.append(
              (F'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')	)
          rename_keys.append(
              (F'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')	)

     rename_keys.append((F'''{old}/embeddings/word_embeddings/weight:0''', F'''{new}.embeddings.word_embeddings.weight''')	)
     rename_keys.append(
         (F'''{old}/embeddings/position_embeddings/embeddings:0''', F'''{new}.embeddings.position_embeddings.weight''')	)
     rename_keys.append(
         (F'''{old}/embeddings/token_type_embeddings/embeddings:0''', F'''{new}.embeddings.token_type_embeddings.weight''')	)
     rename_keys.append((F'''{old}/embeddings/LayerNorm/gamma:0''', F'''{new}.embeddings.LayerNorm.weight''')	)
     rename_keys.append((F'''{old}/embeddings/LayerNorm/beta:0''', F'''{new}.embeddings.LayerNorm.bias''')	)

     rename_keys.append((F'''{old}/pooler/dense/kernel:0''', F'''{new}.pooler.dense.weight''')	)
     rename_keys.append((F'''{old}/pooler/dense/bias:0''', F'''{new}.pooler.dense.bias''')	)
     rename_keys.append(('dense/kernel:0', 'text_projection.weight')	)
     rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
     rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
     rename_keys.append(('temperature:0', 'temperature')	)

     for item in rename_keys:
          if item[0] in original_param_names:
               __lowercase     						=item[1]
     return key_mapping

def 			__UpperCamelCase							(		lowercase__  :					Union[str, Any],   lowercase__  :					Dict,   lowercase__  :					List[Any]	):






     '''simple docstring'''





     list(hf_params.keys()	)

     for key, value in tf_params.items():
          if key not in key_mapping:
               continue

          __lowercase     						=key_mapping[key]
          if "_conv" in key and "kernel" in key:
               __lowercase     						=torch.from_numpy(lowercase__	).permute(3,   2,   0,   1	)
          elif "embeddings" in key:
               __lowercase     						=torch.from_numpy(lowercase__	)
          elif "depthwise_kernel" in key:
               __lowercase     						=torch.from_numpy(lowercase__	).permute(2,   3,   0,   1	)
          elif "kernel" in key:
               __lowercase     						=torch.from_numpy(np.transpose(lowercase__	)	)
          elif "temperature" in key:
               __lowercase     						=value
          elif "bn/gamma" or "bn/beta" in key:
               __lowercase     						=torch.from_numpy(np.transpose(lowercase__	)	).squeeze()
          else:
               __lowercase     						=torch.from_numpy(lowercase__	)

          # Replace HF parameters with original TF model parameters
          hf_params[hf_key].copy_(lowercase__	)

@torch.no_grad()
def 			__UpperCamelCase							(		lowercase__  :					Any,   lowercase__  :					Union[str, Any],   lowercase__  :					str,   lowercase__  :					Optional[Any]	):






     '''simple docstring'''





     __lowercase     						=64
     __lowercase     						=Tokenizer(lowercase__	)
     __lowercase     						=align.Align('efficientnet-b7',   'bert-base',   6_40,   lowercase__,   tok.get_vocab_size()	)
     original_model.compile()
     original_model.load_weights(lowercase__	)

     __lowercase     						=original_model.trainable_variables
     __lowercase     						=original_model.non_trainable_variables
     __lowercase     						={param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
          __lowercase     						=param.numpy()
     __lowercase     						=list(tf_params.keys()	)

     # Load HuggingFace model
     __lowercase     						=get_align_config()
     __lowercase     						=AlignModel(lowercase__	).eval()
     __lowercase     						=hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'	)
     __lowercase     						=rename_keys(lowercase__	)
     replace_params(lowercase__,   lowercase__,   lowercase__	)

     # Initialize processor
     __lowercase     						=get_processor()
     __lowercase     						=processor(
         images=prepare_img(),   text='A picture of a cat',   padding='max_length',   max_length=64,   return_tensors='pt'	)

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          __lowercase     						=hf_model(**lowercase__	)

     __lowercase     						=outputs.image_embeds.detach().numpy()
     __lowercase     						=outputs.text_embeds.detach().numpy()

     # Original model inference
     __lowercase     						=False
     __lowercase     						=EfficientNetImageProcessor(
         do_center_crop=lowercase__,   do_rescale=lowercase__,   do_normalize=lowercase__,   include_top=lowercase__,   resample=Image.BILINEAR,   )
     __lowercase     						=tf_image_processor(images=prepare_img(),   return_tensors='tf',   data_format='channels_last'	)['pixel_values']
     __lowercase     						=tok(tf.constant(['A picture of a cat']	)	)

     __lowercase     						=original_model.image_encoder(lowercase__,   training=lowercase__	)
     __lowercase     						=original_model.text_encoder(lowercase__,   training=lowercase__	)

     __lowercase     						=tf.nn.la_normalize(lowercase__,   axis=-1	)
     __lowercase     						=tf.nn.la_normalize(lowercase__,   axis=-1	)

     # Check whether original and HF model outputs match  -> np.allclose
     if not np.allclose(lowercase__,   lowercase__,   atol=1E-3	):
          raise ValueError('The predicted image features are not the same.'	)
     if not np.allclose(lowercase__,   lowercase__,   atol=1E-3	):
          raise ValueError('The predicted text features are not the same.'	)
     print('Model outputs match!'	)

     if save_model:
          # Create folder to save model
          if not os.path.isdir(lowercase__	):
               os.mkdir(lowercase__	)
          # Save converted model and image processor
          hf_model.save_pretrained(lowercase__	)
          processor.save_pretrained(lowercase__	)

     if push_to_hub:
          # Push model and image processor to hub
          print('Pushing converted ALIGN to the hub...'	)
          processor.push_to_hub('align-base'	)
          hf_model.push_to_hub('align-base'	)


if __name__ == "__main__":
       UpperCAmelCase							=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_path''',
           default='''./weights/model-weights''',
           type=str,
           help='''Path to the pretrained TF ALIGN checkpoint.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''hf_model''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
       parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

       UpperCAmelCase							=					parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


