"""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	:		Tuple   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	int						)  ->							Tuple:
   __lowerCamelCase    		=					tf.image.resize(lowercase_      , (346, 346)						)
   __lowerCamelCase    		=					tf.image.crop_to_bounding_box(lowercase_      , (346 - 289) // 2      , (346 - 289) // 2      , 289      , 289						)
   return image



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
   __lowerCamelCase    		=					289
   __lowerCamelCase    		=					640
   __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=lowercase_      , vision_config=lowercase_      , projection_dim=640						)
   return config



def       A__    (	)  ->							Optional[int]:
   __lowerCamelCase    		=					'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCamelCase    		=					Image.open(requests.get(lowercase_      , stream=lowercase_						).raw						)
   return im



def       A__    (	)  ->							List[Any]:
   __lowerCamelCase    		=					EfficientNetImageProcessor(
       do_center_crop=lowercase_      , rescale_factor=1 / 1_2_7.5      , rescale_offset=lowercase_      , do_normalize=lowercase_      , include_top=lowercase_      , resample=Image.BILINEAR      , )
   __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-uncased'						)
   __lowerCamelCase    		=					64
   __lowerCamelCase    		=					AlignProcessor(image_processor=lowercase_      , tokenizer=lowercase_						)
   return processor



def       A__    (	lowercase_     :	Union[str, Any]						)  ->							List[Any]:
   # EfficientNet image encoder
   __lowerCamelCase    		=					[v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
   __lowerCamelCase    		=					list(set(lowercase_						)						)
   __lowerCamelCase    		=					sorted(lowercase_						)
   __lowerCamelCase    		=					len(lowercase_						)
   __lowerCamelCase    		=					{b: str(lowercase_						) for b, i in zip(lowercase_      , range(lowercase_						)						)}

   __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       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[int]      , lowercase_     :	Union[str, Any]						)  ->							List[str]:
   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(lowercase_						).permute(3      , 2      , 0      , 1						)
      elif "embeddings" in key:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						)
      elif "depthwise_kernel" in key:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						).permute(2      , 3      , 0      , 1						)
      elif "kernel" in key:
         __lowerCamelCase    		=					torch.from_numpy(np.transpose(lowercase_						)						)
      elif "temperature" in key:
         __lowerCamelCase    		=					value
      elif "bn/gamma" or "bn/beta" in key:
         __lowerCamelCase    		=					torch.from_numpy(np.transpose(lowercase_						)						).squeeze()
      else:
         __lowerCamelCase    		=					torch.from_numpy(lowercase_						)

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



@torch.no_grad()
def       A__    (	lowercase_     :	Any      , lowercase_     :	List[Any]      , lowercase_     :	List[str]      , lowercase_     :	Dict						)  ->							int:
   __lowerCamelCase    		=					64
   __lowerCamelCase    		=					Tokenizer(lowercase_						)
   __lowerCamelCase    		=					align.Align('efficientnet-b7'      , 'bert-base'      , 640      , lowercase_      , tok.get_vocab_size()						)
   original_model.compile()
   original_model.load_weights(lowercase_						)

   __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(lowercase_						).eval()
   __lowerCamelCase    		=					hf_model.state_dict()

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

   # 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(**lowercase_						)

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

   # Original model inference
   __lowerCamelCase    		=					False
   __lowerCamelCase    		=					EfficientNetImageProcessor(
       do_center_crop=lowercase_      , do_rescale=lowercase_      , do_normalize=lowercase_      , include_top=lowercase_      , 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(lowercase_      , training=lowercase_						)
   __lowerCamelCase    		=					original_model.text_encoder(lowercase_      , training=lowercase_						)

   __lowerCamelCase    		=					tf.nn.la_normalize(lowercase_      , axis=-1						)
   __lowerCamelCase    		=					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__":
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   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	:		Dict   =   parser.parse_args()
     convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """こん""",
					    """こんに""",
					    """にちは""",
					    """ばんは""",
					    """世界,㔺界""",
					    """、""",
					    """。""",
					    """<BR>""",
					    """<SP>""",
					    """<TAB>""",
					    """<URL>""",
					    """<EMAIL>""",
					    """<TEL>""",
					    """<DATE>""",
					    """<PRICE>""",
					    """<BLOCK>""",
					    """<KIGOU>""",
					    """<U2000U2BFF>""",
					    """<|emoji1|>""",
					    """<unk>""",
					    """<|startoftext|>""",
					    """<|endoftext|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""emoji_file"""]			)
					with open(self.vocab_file  ,    """w"""  ,    encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
					with open(self.emoji_file  ,    """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__UpperCamelCase			)			)
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass