






"""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()
__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = tf.image.resize(a__    ,			(3_46, 3_46)   )
						lowerCAmelCase      :Optional[int]				  = tf.image.crop_to_bounding_box(a__    ,			(3_46 - 2_89) // 2    ,			(3_46 - 2_89) // 2    ,			2_89    ,			2_89   )
						return image


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = EfficientNetConfig.from_pretrained('google/efficientnet-b7'   )
						lowerCAmelCase      :Optional[Any]				  = 2_89
						lowerCAmelCase      :str				  = 6_40
						lowerCAmelCase      :List[str]				  = {'0': 'LABEL_0', '1': 'LABEL_1'}
						lowerCAmelCase      :Any				  = {'LABEL_0': 0, 'LABEL_1': 1}
						lowerCAmelCase      :List[Any]				  = []

						lowerCAmelCase      :str				  = BertConfig()
						lowerCAmelCase      :Union[str, Any]				  = AlignConfig.from_text_vision_configs(
						    text_config=a__    ,			vision_config=a__    ,			projection_dim=6_40   )
						return config


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = 'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase      :List[Any]				  = Image.open(requests.get(a__    ,			stream=a__   ).raw   )
						return im


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = EfficientNetImageProcessor(
						    do_center_crop=a__    ,			rescale_factor=1 / 127.5    ,			rescale_offset=a__    ,			do_normalize=a__    ,			include_top=a__    ,			resample=Image.BILINEAR    ,			)
						lowerCAmelCase      :str				  = BertTokenizer.from_pretrained('bert-base-uncased'   )
						lowerCAmelCase      :Any				  = 64
						lowerCAmelCase      :Optional[Any]				  = AlignProcessor(image_processor=a__    ,			tokenizer=a__   )
						return processor


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = [v.split('_'   )[0].split('block'   )[1] for v in original_param_names if v.startswith('block'   )]
						lowerCAmelCase      :str				  = list(set(a__   )   )
						lowerCAmelCase      :List[str]				  = sorted(a__   )
						lowerCAmelCase      :Union[str, Any]				  = len(a__   )
						lowerCAmelCase      :Tuple				  = {b: str(a__   ) for b, i in zip(a__    ,			range(a__   )   )}

						lowerCAmelCase      :Any				  = []
						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      :int				  = 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      :Tuple				  = {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		lowerCAmelCase      :Optional[int]				  = 'vision_model.' + item[1]

    # BERT text encoder
						lowerCAmelCase      :Optional[Any]				  = []
						lowerCAmelCase      :Optional[int]				  = 'tf_bert_model/bert'
						lowerCAmelCase      :Tuple				  = '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      :Tuple				  = item[1]
						return key_mapping


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						list(hf_params.keys()   )

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

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

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


@torch.no_grad()
def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = 64
						lowerCAmelCase      :Optional[int]				  = Tokenizer(a__   )
						lowerCAmelCase      :List[Any]				  = align.Align('efficientnet-b7'    ,			'bert-base'    ,			6_40    ,			a__    ,			tok.get_vocab_size()   )
						original_model.compile()
						original_model.load_weights(a__   )

						lowerCAmelCase      :List[Any]				  = original_model.trainable_variables
						lowerCAmelCase      :List[str]				  = original_model.non_trainable_variables
						lowerCAmelCase      :int				  = {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												lowerCAmelCase      :Any				  = param.numpy()
						lowerCAmelCase      :Union[str, Any]				  = list(tf_params.keys()   )

						# Load HuggingFace model
						lowerCAmelCase      :List[str]				  = get_align_config()
						lowerCAmelCase      :Any				  = AlignModel(a__   ).eval()
						lowerCAmelCase      :int				  = hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('Converting parameters...'   )
						lowerCAmelCase      :List[str]				  = rename_keys(a__   )
						replace_params(a__    ,			a__    ,			a__   )

						# Initialize processor
						lowerCAmelCase      :List[Any]				  = get_processor()
						lowerCAmelCase      :List[Any]				  = 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      :List[str]				  = hf_model(**a__   )

						lowerCAmelCase      :Tuple				  = outputs.image_embeds.detach().numpy()
						lowerCAmelCase      :Union[str, Any]				  = outputs.text_embeds.detach().numpy()

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

						lowerCAmelCase      :Optional[Any]				  = original_model.image_encoder(a__    ,			training=a__   )
						lowerCAmelCase      :Optional[int]				  = original_model.text_encoder(a__    ,			training=a__   )

						lowerCAmelCase      :Any				  = tf.nn.la_normalize(a__    ,			axis=-1   )
						lowerCAmelCase      :List[Any]				  = 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__":
		__SCREAMING_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')

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







"""simple docstring"""


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).images

											lowerCAmelCase      :List[str]				  = load_pt(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'      )

											assert_mean_pixel_difference(UpperCAmelCase    ,   UpperCAmelCase      )