
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()
_snake_case		  =   logging.get_logger(__name__)
def lowerCamelCase_       (			A			:      str						):




      """simple docstring"""
      lowerCAmelCase_						=      tf.image.resize(A		,					(3_46, 3_46)						)
      lowerCAmelCase_						=      tf.image.crop_to_bounding_box(A		,					(3_46 - 2_89) // 2		,					(3_46 - 2_89) // 2		,					2_89		,					2_89						)
      return image
def lowerCamelCase_       (			):




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

      lowerCAmelCase_						=      BertConfig()
      lowerCAmelCase_						=      AlignConfig.from_text_vision_configs(
          text_config=A		,					vision_config=A		,					projection_dim=6_40						)
      return config
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase_						=      Image.open(requests.get(A		,					stream=A						).raw						)
      return im
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      EfficientNetImageProcessor(
          do_center_crop=A		,					rescale_factor=1 / 1_2_7.5		,					rescale_offset=A		,					do_normalize=A		,					include_top=A		,					resample=Image.BILINEAR		,					)
      lowerCAmelCase_						=      BertTokenizer.from_pretrained('''bert-base-uncased'''						)
      lowerCAmelCase_						=      64
      lowerCAmelCase_						=      AlignProcessor(image_processor=A		,					tokenizer=A						)
      return processor
def lowerCamelCase_       (			A			:      List[str]						):




      """simple docstring"""
      # EfficientNet image encoder
      lowerCAmelCase_						=      [v.split('''_'''						)[0].split('''block'''						)[1] for v in original_param_names if v.startswith('''block'''						)]
      lowerCAmelCase_						=      list(set(A						)						)
      lowerCAmelCase_						=      sorted(A						)
      lowerCAmelCase_						=      len(A						)
      lowerCAmelCase_						=      {b: str(A						) for b, i in zip(A		,					range(A						)						)}

      lowerCAmelCase_						=      []
      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:
            lowerCAmelCase_						=      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')						)

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

    # BERT text encoder
      lowerCAmelCase_						=      []
      lowerCAmelCase_						=      '''tf_bert_model/bert'''
      lowerCAmelCase_						=      '''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:
                  lowerCAmelCase_						=      item[1]
      return key_mapping
def lowerCamelCase_       (			A			:      Optional[int]		,					A			:      Tuple		,					A			:      List[Any]						):




      """simple docstring"""
      list(hf_params.keys()						)

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

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

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(A						)
@torch.no_grad()
def lowerCamelCase_       (			A			:      List[str]		,					A			:      Union[str, Any]		,					A			:      Union[str, Any]		,					A			:      int						):




      """simple docstring"""
      lowerCAmelCase_						=      64
      lowerCAmelCase_						=      Tokenizer(A						)
      lowerCAmelCase_						=      align.Align('''efficientnet-b7'''		,					'''bert-base'''		,					6_40		,					A		,					tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(A						)

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

      # Load HuggingFace model
      lowerCAmelCase_						=      get_align_config()
      lowerCAmelCase_						=      AlignModel(A						).eval()
      lowerCAmelCase_						=      hf_model.state_dict()

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

      # Initialize processor
      lowerCAmelCase_						=      get_processor()
      lowerCAmelCase_						=      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():
            lowerCAmelCase_						=      hf_model(**A						)

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

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

      lowerCAmelCase_						=      original_model.image_encoder(A		,					training=A						)
      lowerCAmelCase_						=      original_model.text_encoder(A		,					training=A						)

      lowerCAmelCase_						=      tf.nn.la_normalize(A		,					axis=-1						)
      lowerCAmelCase_						=      tf.nn.la_normalize(A		,					axis=-1						)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(A		,					A		,					atol=1e-3						):
            raise ValueError('''The predicted image features are not the same.'''						)
      if not np.allclose(A		,					A		,					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(A						):
                  os.mkdir(A						)
            # Save converted model and image processor
            hf_model.save_pretrained(A						)
            processor.save_pretrained(A						)

      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__":
 _snake_case		  =   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")

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





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







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







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



