




"""simple docstring"""



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_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			List[str]      ,					snake_case_		:			List[Any]=13      ,					snake_case_		:			int=7      ,					snake_case_		:			List[Any]=True      ,					snake_case_		:			Tuple=True      ,					snake_case_		:			Tuple=False      ,					snake_case_		:			str=True      ,					snake_case_		:			str=99      ,					snake_case_		:			int=64      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Optional[int]=4      ,					snake_case_		:			List[Any]=64      ,					snake_case_		:			Any="gelu"      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			List[Any]=0.1      ,					snake_case_		:			Dict=512      ,					snake_case_		:			Dict=16      ,					snake_case_		:			Tuple=2      ,					snake_case_		:			Tuple=0.02      ,					snake_case_		:			Optional[Any]=3      ,					snake_case_		:			Dict=4      ,					snake_case_		:			List[str]=None      ,					) ->				Any:




     '''simple docstring'''

     A__			=							parent
     A__			=							batch_size
     A__			=							seq_length
     A__			=							is_training
     A__			=							use_input_mask
     A__			=							use_token_type_ids
     A__			=							use_labels
     A__			=							vocab_size
     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							max_position_embeddings
     A__			=							type_vocab_size
     A__			=							type_sequence_label_size
     A__			=							initializer_range
     A__			=							num_labels
     A__			=							num_choices
     A__			=							scope




    def 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

     A__			=							None
     if self.use_input_mask:
      A__			=							random_attention_mask([self.batch_size, self.seq_length] )

     A__			=							None
     A__			=							None
     A__			=							None
     if self.use_labels:
      A__			=							ids_tensor([self.batch_size]      ,					self.type_sequence_label_size )
      A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels )
      A__			=							ids_tensor([self.batch_size]      ,					self.num_choices )

     A__			=							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 				__magic_name__				(       self		:			str      ,					snake_case_		:			int      ,					snake_case_		:			int      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			Any      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetModel(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     A__			=							[input_ids, input_mask]
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			str      ,					snake_case_		:			str ) ->				int:




     '''simple docstring'''

     A__			=							TFMPNetForMaskedLM(config=snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size) )




    def 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Any      ,					snake_case_		:			str      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__			=							TFMPNetForQuestionAnswering(config=snake_case_ )
     A__			=							{
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__			=							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 				__magic_name__				(       self		:			List[Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Dict      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			Optional[int] ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForSequenceClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels) )




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			Tuple      ,					snake_case_		:			int      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			str      ,					snake_case_		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.num_choices
     A__			=							TFMPNetForMultipleChoice(snake_case_ )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							tf.tile(tf.expand_dims(snake_case_      ,					1 )      ,					(1, self.num_choices, 1) )
     A__			=							{
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices) )




    def 				__magic_name__				(       self		:			List[str]      ,					snake_case_		:			str      ,					snake_case_		:			Tuple      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[Any]      ,					snake_case_		:			List[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.num_labels
     A__			=							TFMPNetForTokenClassification(snake_case_ )
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     A__			=							model(snake_case_ )
     self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels) )




    def 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     A__			=							self.prepare_config_and_inputs()
     ((A__), (A__), (A__), (A__), (A__), (A__))			=							config_and_inputs
     A__			=							{"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (
        (
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetModel,
        )
        if is_tf_available()
        else ()
    )
    lowercase__		   = (
        {
            '''feature-extraction''': TFMPNetModel,
            '''fill-mask''': TFMPNetForMaskedLM,
            '''question-answering''': TFMPNetForQuestionAnswering,
            '''text-classification''': TFMPNetForSequenceClassification,
            '''token-classification''': TFMPNetForTokenClassification,
            '''zero-shot''': TFMPNetForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowercase__		   = False
    lowercase__		   = False




    def 				__magic_name__				(       self		:			Any ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModelTester(self )
     A__			=							ConfigTester(self      ,					config_class=snake_case_      ,					hidden_size=37 )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Tuple:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case_ )




    def 				__magic_name__				(       self		:			Dict ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case_ )




    def 				__magic_name__				(       self		:			int ) ->				List[Any]:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case_ )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case_ )




    def 				__magic_name__				(       self		:			Tuple ) ->				Any:




     '''simple docstring'''

     A__			=							self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case_ )




    @slow
    def 				__magic_name__				(       self		:			Dict ) ->				Tuple:




     '''simple docstring'''

     for model_name in ["microsoft/mpnet-base"]:
      A__			=							TFMPNetModel.from_pretrained(snake_case_ )
      self.assertIsNotNone(snake_case_ )






@require_tf
class       UpperCAmelCase_    (		unittest.TestCase				):




    @slow
    def 				__magic_name__				(       self		:			str ) ->				List[str]:




     '''simple docstring'''

     A__			=							TFMPNetModel.from_pretrained("microsoft/mpnet-base" )
     A__			=							tf.constant([[0, 1, 2, 3, 4, 5]] )
     A__			=							model(snake_case_ )[0]

     A__			=							[1, 6, 768]
     self.assertEqual(output.shape      ,					snake_case_ )

     A__			=							tf.constant(
         [
             [
                 [-0.106_7172, 0.0821_6473, 0.002_4543],
                 [-0.0346_5879, 0.835_4118, -0.0325_2288],
                 [-0.0656_9476, -0.1242_4111, -0.049_4436],
             ]
         ] )
     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()
SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						str:
 A__			=							tf.image.resize(lowercase_     ,					(3_46, 3_46)		)
 A__			=							tf.image.crop_to_bounding_box(lowercase_     ,					(3_46 - 2_89) // 2     ,					(3_46 - 2_89) // 2     ,					2_89     ,					2_89		)
 return image







def     _SCREAMING_SNAKE_CASE  (				)		->						Dict:
 A__			=							EfficientNetConfig.from_pretrained("google/efficientnet-b7"		)
 A__			=							2_89
 A__			=							6_40
 A__			=							{"0": "LABEL_0", "1": "LABEL_1"}
 A__			=							{"LABEL_0": 0, "LABEL_1": 1}
 A__			=							[]

 A__			=							BertConfig()
 A__			=							AlignConfig.from_text_vision_configs(
     text_config=lowercase_     ,					vision_config=lowercase_     ,					projection_dim=6_40		)
 return config







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







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







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

 A__			=							[]
 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:
  A__			=							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""")		)

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

    # BERT text encoder
 A__			=							[]
 A__			=							"tf_bert_model/bert"
 A__			=							"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:
   A__			=							item[1]
 return key_mapping







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Optional[Any]:
 list(hf_params.keys()		)

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

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

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







@torch.no_grad()
def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_		)		->						int:
 A__			=							64
 A__			=							Tokenizer(lowercase_		)
 A__			=							align.Align("efficientnet-b7"     ,					"bert-base"     ,					6_40     ,					lowercase_     ,					tok.get_vocab_size()		)
 original_model.compile()
 original_model.load_weights(lowercase_		)

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

 # Load HuggingFace model
 A__			=							get_align_config()
 A__			=							AlignModel(lowercase_		).eval()
 A__			=							hf_model.state_dict()

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

 # Initialize processor
 A__			=							get_processor()
 A__			=							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():
  A__			=							hf_model(**lowercase_		)

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

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

 A__			=							original_model.image_encoder(lowercase_     ,					training=lowercase_		)
 A__			=							original_model.text_encoder(lowercase_     ,					training=lowercase_		)

 A__			=							tf.nn.la_normalize(lowercase_     ,					axis=-1		)
 A__			=							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							=			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)





