


from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.mpnet.modeling_tf_mpnet import (
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetModel,
  )



class       UpperCAmelCase_		:
       '''simple docstring'''






       def __init__(   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE=13       ,    __SCREAMING_SNAKE_CASE=7       ,    __SCREAMING_SNAKE_CASE=True       ,    __SCREAMING_SNAKE_CASE=True       ,    __SCREAMING_SNAKE_CASE=False       ,    __SCREAMING_SNAKE_CASE=True       ,    __SCREAMING_SNAKE_CASE=99       ,    __SCREAMING_SNAKE_CASE=64       ,    __SCREAMING_SNAKE_CASE=2       ,    __SCREAMING_SNAKE_CASE=4       ,    __SCREAMING_SNAKE_CASE=64       ,    __SCREAMING_SNAKE_CASE="gelu"       ,    __SCREAMING_SNAKE_CASE=0.1       ,    __SCREAMING_SNAKE_CASE=0.1       ,    __SCREAMING_SNAKE_CASE=512       ,    __SCREAMING_SNAKE_CASE=16       ,    __SCREAMING_SNAKE_CASE=2       ,    __SCREAMING_SNAKE_CASE=0.02       ,    __SCREAMING_SNAKE_CASE=3       ,    __SCREAMING_SNAKE_CASE=4       ,    __SCREAMING_SNAKE_CASE=None       ,    ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						parent
              UpperCamelCase			:		Any      		=						batch_size
              UpperCamelCase			:		Optional[int]      		=						seq_length
              UpperCamelCase			:		Dict      		=						is_training
              UpperCamelCase			:		List[str]      		=						use_input_mask
              UpperCamelCase			:		Dict      		=						use_token_type_ids
              UpperCamelCase			:		str      		=						use_labels
              UpperCamelCase			:		Tuple      		=						vocab_size
              UpperCamelCase			:		Optional[Any]      		=						hidden_size
              UpperCamelCase			:		int      		=						num_hidden_layers
              UpperCamelCase			:		Optional[Any]      		=						num_attention_heads
              UpperCamelCase			:		List[str]      		=						intermediate_size
              UpperCamelCase			:		Tuple      		=						hidden_act
              UpperCamelCase			:		List[Any]      		=						hidden_dropout_prob
              UpperCamelCase			:		List[Any]      		=						attention_probs_dropout_prob
              UpperCamelCase			:		Tuple      		=						max_position_embeddings
              UpperCamelCase			:		int      		=						type_vocab_size
              UpperCamelCase			:		str      		=						type_sequence_label_size
              UpperCamelCase			:		Union[str, Any]      		=						initializer_range
              UpperCamelCase			:		int      		=						num_labels
              UpperCamelCase			:		Any      		=						num_choices
              UpperCamelCase			:		Optional[Any]      		=						scope






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		str      		=						ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size  )

              UpperCamelCase			:		Any      		=						None
              if self.use_input_mask:
                     UpperCamelCase			:		List[Any]      		=						random_attention_mask([self.batch_size, self.seq_length]  )

              UpperCamelCase			:		int      		=						None
              UpperCamelCase			:		List[Any]      		=						None
              UpperCamelCase			:		Union[str, Any]      		=						None
              if self.use_labels:
                     UpperCamelCase			:		List[Any]      		=						ids_tensor([self.batch_size]       ,    self.type_sequence_label_size  )
                     UpperCamelCase			:		Dict      		=						ids_tensor([self.batch_size, self.seq_length]       ,    self.num_labels  )
                     UpperCamelCase			:		Optional[Any]      		=						ids_tensor([self.batch_size]       ,    self.num_choices  )

              UpperCamelCase			:		int      		=						MPNetConfig(
                  vocab_size=self.vocab_size       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    intermediate_size=self.intermediate_size       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    max_position_embeddings=self.max_position_embeddings       ,    initializer_range=self.initializer_range       ,    )
              return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[Any]      		=						TFMPNetModel(config=_snake_case  )
              UpperCamelCase			:		List[Any]      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              UpperCamelCase			:		Optional[Any]      		=						model(_snake_case  )
              UpperCamelCase			:		Optional[int]      		=						[input_ids, input_mask]
              UpperCamelCase			:		int      		=						model(_snake_case  )
              self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						TFMPNetForMaskedLM(config=_snake_case  )
              UpperCamelCase			:		int      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              UpperCamelCase			:		Any      		=						model(_snake_case  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						TFMPNetForQuestionAnswering(config=_snake_case  )
              UpperCamelCase			:		Any      		=						{
                  '''input_ids''': input_ids,
                  '''attention_mask''': input_mask,
              }
              UpperCamelCase			:		List[str]      		=						model(_snake_case  )
              self.parent.assertEqual(result.start_logits.shape       ,    (self.batch_size, self.seq_length)  )
              self.parent.assertEqual(result.end_logits.shape       ,    (self.batch_size, self.seq_length)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		str      		=						self.num_labels
              UpperCamelCase			:		Union[str, Any]      		=						TFMPNetForSequenceClassification(_snake_case  )
              UpperCamelCase			:		Optional[int]      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              UpperCamelCase			:		Optional[int]      		=						model(_snake_case  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Union[str, Any]      		=						self.num_choices
              UpperCamelCase			:		Optional[int]      		=						TFMPNetForMultipleChoice(_snake_case  )
              UpperCamelCase			:		List[str]      		=						tf.tile(tf.expand_dims(_snake_case       ,    1  )       ,    (1, self.num_choices, 1)  )
              UpperCamelCase			:		Optional[int]      		=						tf.tile(tf.expand_dims(_snake_case       ,    1  )       ,    (1, self.num_choices, 1)  )
              UpperCamelCase			:		List[str]      		=						{
                  '''input_ids''': multiple_choice_inputs_ids,
                  '''attention_mask''': multiple_choice_input_mask,
              }
              UpperCamelCase			:		Union[str, Any]      		=						model(_snake_case  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_choices)  )






       def        _lowercase      (   self       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						self.num_labels
              UpperCamelCase			:		Union[str, Any]      		=						TFMPNetForTokenClassification(_snake_case  )
              UpperCamelCase			:		List[str]      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              UpperCamelCase			:		Optional[int]      		=						model(_snake_case  )
              self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.num_labels)  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[Any]      		=						self.prepare_config_and_inputs()
              ((UpperCamelCase)		,       (UpperCamelCase)		,       (UpperCamelCase)		,       (UpperCamelCase)		,       (UpperCamelCase)		,       (UpperCamelCase))			:		str      		=						config_and_inputs
              UpperCamelCase			:		Optional[Any]      		=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              return config, inputs_dict




@require_tf
class       UpperCAmelCase_		(							a__,   a__,   unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase					:   List[Any]							=    (
           (
               TFMPNetForMaskedLM,
               TFMPNetForMultipleChoice,
               TFMPNetForQuestionAnswering,
               TFMPNetForSequenceClassification,
               TFMPNetForTokenClassification,
               TFMPNetModel,
           )
           if is_tf_available()
           else ()
       )
       __UpperCamelCase					:   List[str]							=    (
           {
               "feature-extraction": TFMPNetModel,
               "fill-mask": TFMPNetForMaskedLM,
               "question-answering": TFMPNetForQuestionAnswering,
               "text-classification": TFMPNetForSequenceClassification,
               "token-classification": TFMPNetForTokenClassification,
               "zero-shot": TFMPNetForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __UpperCamelCase					:   Union[str, Any]							=    False
       __UpperCamelCase					:   Optional[Any]							=    False






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						TFMPNetModelTester(self  )
              UpperCamelCase			:		List[str]      		=						ConfigTester(self       ,    config_class=_snake_case       ,    hidden_size=37  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              self.config_tester.run_common_tests()






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Optional[int]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_model(*_snake_case  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_masked_lm(*_snake_case  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Any      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_question_answering(*_snake_case  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[str]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_sequence_classification(*_snake_case  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Union[str, Any]      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_multiple_choice(*_snake_case  )






       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_token_classification(*_snake_case  )






       @slow
       def        _lowercase      (   self  ):




              """simple docstring"""




              for model_name in ["microsoft/mpnet-base"]:
                     UpperCamelCase			:		Optional[int]      		=						TFMPNetModel.from_pretrained(_snake_case  )
                     self.assertIsNotNone(_snake_case  )




@require_tf
class       UpperCAmelCase_		(							unittest.TestCase):
       '''simple docstring'''






       @slow
       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		List[str]      		=						TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
              UpperCamelCase			:		Optional[Any]      		=						tf.constant([[0, 1, 2, 3, 4, 5]]  )
              UpperCamelCase			:		Optional[Any]      		=						model(_snake_case  )[0]

              UpperCamelCase			:		Optional[int]      		=						[1, 6, 768]
              self.assertEqual(output.shape       ,    _snake_case  )

              UpperCamelCase			:		int      		=						tf.constant(
                  [
                      [
                          [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                          [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                          [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                      ]
                  ]  )
              tf.debugging.assert_near(output[:, :3, :3]       ,    _snake_case       ,    atol=1e-4  )








"""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			:		Union[str, Any]		    =   logging.get_logger(__name__)

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]       ):
 lowerCAmelCase  							=      tf.image.resize(_UpperCAmelCase   ,			(346, 346)       )
 lowerCAmelCase  							=      tf.image.crop_to_bounding_box(_UpperCAmelCase   ,			(346 - 289) // 2   ,			(346 - 289) // 2   ,			289   ,			289       )
 return image

def 						_SCREAMING_SNAKE_CASE       ():
 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=_UpperCAmelCase   ,			vision_config=_UpperCAmelCase   ,			projection_dim=640       )
 return config

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase  							=      Image.open(requests.get(_UpperCAmelCase   ,			stream=_UpperCAmelCase       ).raw       )
 return im

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

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]       ):
 # EfficientNet image encoder
 lowerCAmelCase  							=      [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
 lowerCAmelCase  							=      list(set(_UpperCAmelCase       )       )
 lowerCAmelCase  							=      sorted(_UpperCAmelCase       )
 lowerCAmelCase  							=      len(_UpperCAmelCase       )
 lowerCAmelCase  							=      {b: str(_UpperCAmelCase       ) for b, i in zip(_UpperCAmelCase   ,			range(_UpperCAmelCase       )       )}

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

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

@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Any       ):
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      Tokenizer(_UpperCAmelCase       )
 lowerCAmelCase  							=      align.Align('efficientnet-b7'   ,			'bert-base'   ,			640   ,			_UpperCAmelCase   ,			tok.get_vocab_size()       )
 original_model.compile()
 original_model.load_weights(_UpperCAmelCase       )

 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(_UpperCAmelCase       ).eval()
 lowerCAmelCase  							=      hf_model.state_dict()

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

 # 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(**_UpperCAmelCase       )

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

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

 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )
 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )

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

 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			:		str		    =   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			:		int		    =   parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





