





from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
  from transformers import (
      TFBertModel,
      TFCLIPVisionModel,
      TFDeiTModel,
      TFRobertaModel,
      TFVisionTextDualEncoderModel,
      TFViTModel,
      VisionTextDualEncoderConfig,
  )

if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor






def       _A  (      lowerCamelCase   ):
   if isinstance(lowerCamelCase							, collections.abc.Iterable   ):
      return x
   return (x, x)







@require_tf
class   __lowerCAmelCase			:





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  List[str]:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      pass





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  VisionTextDualEncoderConfig.from_vision_text_configs(snake_case					,			snake_case      )

      a__	:						Tuple      		=  TFVisionTextDualEncoderModel(snake_case      )

      a__	:						List[Any]      		=  model(input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case      )

      self.assertEqual(output["text_embeds"].shape					,			(input_ids.shape[0], config.projection_dim)      )
      self.assertEqual(output["image_embeds"].shape					,			(pixel_values.shape[0], config.projection_dim)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.get_vision_text_model(snake_case					,			snake_case      )
      a__	:						Optional[Any]      		=  TFVisionTextDualEncoderModel(vision_model=snake_case					,			text_model=snake_case      )

      a__	:						Tuple      		=  model(input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case      )

      self.assertEqual(output["text_embeds"].shape					,			(input_ids.shape[0], model.config.projection_dim)      )
      self.assertEqual(output["image_embeds"].shape					,			(pixel_values.shape[0], model.config.projection_dim)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Optional[int]:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.get_vision_text_model(snake_case					,			snake_case      )
      a__	:						Tuple      		=  {"vision_model": vision_model, "text_model": text_model}
      a__	:						Optional[Any]      		=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case      )

      a__	:						str      		=  model(input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case      )

      self.assertEqual(output["text_embeds"].shape					,			(input_ids.shape[0], model.config.projection_dim)      )
      self.assertEqual(output["image_embeds"].shape					,			(pixel_values.shape[0], model.config.projection_dim)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Any:
      """simple docstring"""





      a__	:						Dict      		=  self.get_vision_text_model(snake_case					,			snake_case      )
      a__	:						Dict      		=  TFVisionTextDualEncoderModel(vision_model=snake_case					,			text_model=snake_case      )

      a__	:						Dict      		=  model(input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case      )
      a__	:						Optional[int]      		=  output[0].numpy()

      with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(snake_case      )
         a__	:						int      		=  TFVisionTextDualEncoderModel.from_pretrained(snake_case      )

         a__	:						int      		=  model(input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case      )
         a__	:						Optional[Any]      		=  after_output[0].numpy()
         a__	:						Any      		=  np.amax(np.abs(out_a - out_a      )      )
         self.assertLessEqual(snake_case					,			1E-5      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  self.get_vision_text_model(snake_case					,			snake_case      )
      a__	:						int      		=  TFVisionTextDualEncoderModel(vision_model=snake_case					,			text_model=snake_case      )

      a__	:						List[Any]      		=  model(
          input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case					,			output_attentions=snake_case      )

      a__	:						int      		=  output.vision_model_output.attentions
      self.assertEqual(len(snake_case      )					,			vision_config.num_hidden_layers      )

      # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
      a__	:						Optional[int]      		=  to_atuple(vision_model.config.image_size      )
      a__	:						List[str]      		=  to_atuple(vision_model.config.patch_size      )
      a__	:						Dict      		=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      a__	:						Optional[Any]      		=  num_patches + 1
      self.assertEqual(vision_attentions[0].shape[-3:]					,			(vision_config.num_attention_heads, seq_len, seq_len)      )

      a__	:						Any      		=  output.text_model_output.attentions
      self.assertEqual(len(snake_case      )					,			text_config.num_hidden_layers      )

      self.assertEqual(
          text_attentions[0].shape[-3:]					,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,			)





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  np.abs((a - b)      ).max()
      self.assertLessEqual(snake_case					,			snake_case					,			F"""Difference between torch and flax is {diff} (>= {tol})."""      )





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						str      		=  self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**snake_case      )





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						List[str]      		=  self.prepare_config_and_inputs()
      self.check_save_load(**snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**snake_case      )





   @slow
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Tuple      		=  self.get_pretrained_model_and_inputs()

      a__	:						List[str]      		=  model_a(**snake_case      )
      a__	:						Optional[Any]      		=  outputs[0].numpy()

      with tempfile.TemporaryDirectory() as tmp_dirname:
         model_a.save_pretrained(snake_case      )
         a__	:						str      		=  TFVisionTextDualEncoderModel.from_pretrained(snake_case      )

         a__	:						Tuple      		=  model_a(**snake_case      )
         a__	:						Tuple      		=  after_outputs[0].numpy()
         a__	:						str      		=  np.amax(np.abs(out_a - out_a      )      )
         self.assertLessEqual(snake_case					,			1E-5      )







@require_tf
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "hf-internal-testing/tiny-random-vit"					,			"hf-internal-testing/tiny-random-bert"      )
      a__	:						int      		=  13
      a__	:						Any      		=  floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]      )
      a__	:						Any      		=  ids_tensor([batch_size, 4]					,			model.text_model.config.vocab_size      )
      a__	:						int      		=  random_attention_mask([batch_size, 4]      )
      a__	:						Optional[int]      		=  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

      return model, inputs





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						List[str]      		=  TFViTModel(snake_case					,			name="vision_model"      )
      a__	:						List[Any]      		=  TFBertModel(snake_case					,			name="text_model"      )
      return vision_model, text_model





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  TFViTModelTester(self      )
      a__	:						Any      		=  TFBertModelTester(self      )
      a__	:						str      		=  vit_model_tester.prepare_config_and_inputs()
      a__	:						str      		=  bert_model_tester.prepare_config_and_inputs()

      a__	:						Dict      		=  vision_config_and_inputs

      (
          a__     
      )	:						List[Any]      		=  text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_tf
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[str]      		=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "Rocketknight1/tiny-random-deit-tf"					,			"hf-internal-testing/tiny-random-roberta"      )
      a__	:						str      		=  13
      a__	:						str      		=  floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]      )
      a__	:						Any      		=  ids_tensor([batch_size, 4]					,			model.text_model.config.vocab_size      )
      a__	:						List[Any]      		=  random_attention_mask([batch_size, 4]      )
      a__	:						Tuple      		=  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

      return model, inputs





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Tuple:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.get_vision_text_model(snake_case					,			snake_case      )
      a__	:						str      		=  TFVisionTextDualEncoderModel(vision_model=snake_case					,			text_model=snake_case      )

      a__	:						int      		=  model(
          input_ids=snake_case					,			pixel_values=snake_case					,			attention_mask=snake_case					,			output_attentions=snake_case      )

      a__	:						Optional[int]      		=  output.vision_model_output.attentions
      self.assertEqual(len(snake_case      )					,			vision_config.num_hidden_layers      )

      # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
      a__	:						Union[str, Any]      		=  to_atuple(vision_model.config.image_size      )
      a__	:						Tuple      		=  to_atuple(vision_model.config.patch_size      )
      a__	:						List[Any]      		=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      a__	:						int      		=  num_patches + 2
      self.assertEqual(vision_attentions[0].shape[-3:]					,			(vision_config.num_attention_heads, seq_len, seq_len)      )

      a__	:						int      		=  output.text_model_output.attentions
      self.assertEqual(len(snake_case      )					,			text_config.num_hidden_layers      )

      self.assertEqual(
          text_attentions[0].shape[-3:]					,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])					,			)





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  TFDeiTModel(snake_case					,			name="vision_model"      )
      a__	:						int      		=  TFRobertaModel(snake_case					,			name="text_model"      )
      return vision_model, text_model





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Dict      		=  TFDeiTModelTester(self      )
      a__	:						Optional[int]      		=  TFRobertaModelTester(self      )
      a__	:						Dict      		=  vit_model_tester.prepare_config_and_inputs()
      a__	:						Dict      		=  bert_model_tester.prepare_config_and_inputs()

      a__	:						Any      		=  vision_config_and_inputs

      (
          a__     
      )	:						Optional[int]      		=  text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_tf
class   __lowerCAmelCase			(					_UpperCamelCase		,unittest.TestCase  ):





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						List[str]      		=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          "Rocketknight1/tiny-random-clip-tf"					,			"hf-internal-testing/tiny-random-bert"      )
      a__	:						List[str]      		=  13
      a__	:						Any      		=  floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]      )
      a__	:						Optional[int]      		=  ids_tensor([batch_size, 4]					,			model.text_model.config.vocab_size      )
      a__	:						Dict      		=  random_attention_mask([batch_size, 4]      )
      a__	:						Optional[int]      		=  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

      return model, inputs





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Dict      		=  TFCLIPVisionModel(snake_case					,			name="vision_model"      )
      a__	:						Union[str, Any]      		=  TFBertModel(snake_case					,			name="text_model"      )
      return vision_model, text_model





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Optional[int]      		=  TFCLIPVisionModelTester(self      )
      a__	:						List[Any]      		=  TFBertModelTester(self      )
      a__	:						Tuple      		=  clip_model_tester.prepare_config_and_inputs()
      a__	:						List[Any]      		=  bert_model_tester.prepare_config_and_inputs()

      a__	:						str      		=  vision_config_and_inputs

      (
          a__     
      )	:						List[Any]      		=  text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_vision
@require_tf
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @slow
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  TFVisionTextDualEncoderModel.from_pretrained(
          "clip-italian/clip-italian"					,			logit_scale_init_value=1.0					,			from_pt=snake_case      )
      a__	:						Dict      		=  VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"      )

      a__	:						int      		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
      a__	:						Optional[Any]      		=  processor(
          text=["una foto di un gatto", "una foto di un cane"]					,			images=snake_case					,			padding=snake_case					,			return_tensors="np"      )

      a__	:						List[Any]      		=  model(**snake_case      )

      # verify the logits
      self.assertEqual(outputs.logits_per_image.shape					,			(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])      )
      self.assertEqual(
          outputs.logits_per_text.shape					,			(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])					,			)

      a__	:						Optional[Any]      		=  np.array([[1.2_284_727, 0.3_104_122]]      )

      self.assertTrue(np.allclose(outputs.logits_per_image.numpy()					,			snake_case					,			atol=1E-3      )      )








import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  old_name

   if "patch_embed" in old_name:
      a__		,     a__		,     a__	:						Union[str, Any]      		=  old_name.split("."   )

      if layer == "0":
         a__	:						Union[str, Any]      		=  old_name.replace("0"							, "convolution1"   )
      elif layer == "1":
         a__	:						Dict      		=  old_name.replace("1"							, "batchnorm_before"   )
      elif layer == "3":
         a__	:						List[str]      		=  old_name.replace("3"							, "convolution2"   )
      else:
         a__	:						Optional[Any]      		=  old_name.replace("4"							, "batchnorm_after"   )

   if "network" in old_name and re.search(r"\d\.\d"							, lowerCamelCase   ):
      a__	:						List[str]      		=  r"\b\d{2}\b"
      if bool(re.search(lowerCamelCase							, lowerCamelCase   )   ):
         a__	:						Optional[int]      		=  re.search(r"\d\.\d\d."							, lowerCamelCase   ).group()
      else:
         a__	:						Any      		=  re.search(r"\d\.\d."							, lowerCamelCase   ).group()
      if int(match[0]   ) < 6:
         a__	:						List[Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         a__	:						int      		=  trimmed_name.replace("network"							, match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
         a__	:						List[Any]      		=  "intermediate_stages." + trimmed_name
      else:
         a__	:						Union[str, Any]      		=  old_name.replace(lowerCamelCase							, ""   )
         if int(match[2]   ) < num_meta4D_last_stage:
            a__	:						Optional[Any]      		=  trimmed_name.replace("network"							, "meta4D_layers.blocks." + match[2]   )
         else:
            a__	:						Union[str, Any]      		=  str(int(match[2]   ) - num_meta4D_last_stage   )
            a__	:						str      		=  trimmed_name.replace("network"							, "meta3D_layers.blocks." + layer_index   )
            if "norm1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("norm1"							, "layernorm1"   )
            elif "norm2" in old_name:
               a__	:						Optional[int]      		=  trimmed_name.replace("norm2"							, "layernorm2"   )
            elif "fc1" in old_name:
               a__	:						List[str]      		=  trimmed_name.replace("fc1"							, "linear_in"   )
            elif "fc2" in old_name:
               a__	:						Any      		=  trimmed_name.replace("fc2"							, "linear_out"   )

         a__	:						Any      		=  "last_stage." + trimmed_name

   elif "network" in old_name and re.search(r".\d."							, lowerCamelCase   ):
      a__	:						List[str]      		=  old_name.replace("network"							, "intermediate_stages"   )

   if "fc" in new_name:
      a__	:						str      		=  new_name.replace("fc"							, "convolution"   )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      a__	:						str      		=  new_name.replace("norm1"							, "batchnorm_before"   )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      a__	:						Any      		=  new_name.replace("norm2"							, "batchnorm_after"   )
   if "proj" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("proj"							, "projection"   )
   if "dist_head" in new_name:
      a__	:						Tuple      		=  new_name.replace("dist_head"							, "distillation_classifier"   )
   elif "head" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("head"							, "classifier"   )
   elif "patch_embed" in new_name:
      a__	:						Tuple      		=  "efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      a__	:						Union[str, Any]      		=  new_name.replace("norm"							, "layernorm"   )
      a__	:						Optional[int]      		=  "efficientformer." + new_name
   else:
      a__	:						List[Any]      		=  "efficientformer.encoder." + new_name

   return new_name






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for key in checkpoint.copy().keys():
      a__	:						Optional[Any]      		=  checkpoint.pop(lowerCamelCase   )
      a__	:						Dict      		=  val

   return checkpoint






def       _A  (      ):
   a__	:						Tuple      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )["model"]
   a__	:						str      		=  EfficientFormerConfig.from_json_file(lowerCamelCase   )
   a__	:						int      		=  EfficientFormerForImageClassificationWithTeacher(lowerCamelCase   )
   a__	:						Optional[Any]      		=  "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

   a__	:						Tuple      		=  config.depths[-1] - config.num_metaad_blocks + 1
   a__	:						Union[str, Any]      		=  convert_torch_checkpoint(lowerCamelCase							, lowerCamelCase   )

   model.load_state_dict(lowerCamelCase   )
   model.eval()

   a__	:						Dict      		=  {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   a__	:						str      		=  prepare_img()
   a__	:						Dict      		=  256
   a__	:						Union[str, Any]      		=  224
   a__	:						List[str]      		=  EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}							, crop_size={"height": crop_size, "width": crop_size}							, resample=pillow_resamplings["bicubic"]							, )
   a__	:						List[str]      		=  processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

   # original processing pipeline
   a__	:						List[str]      		=  Compose(
       [
           Resize(lowerCamelCase							, interpolation=pillow_resamplings["bicubic"]   ),
           CenterCrop(lowerCamelCase   ),
           ToTensor(),
           Normalize(lowerCamelCase							, lowerCamelCase   ),
       ]   )
   a__	:						List[Any]      		=  image_transforms(lowerCamelCase   ).unsqueeze(0   )

   assert torch.allclose(lowerCamelCase							, lowerCamelCase   )

   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						Any      		=  outputs.logits

   a__	:						Optional[Any]      		=  (1, 1000)

   if "l1" in model_name:
      a__	:						Tuple      		=  torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__	:						int      		=  torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]   )
      assert torch.allclose(logits[0, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__	:						Optional[Any]      		=  torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]   )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""   )

   # Save Checkpoints
   Path(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
   model.save_pretrained(lowerCamelCase   )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
   processor.save_pretrained(lowerCamelCase   )
   print(F"""Processor successfuly saved at {pytorch_dump_path}"""   )

   if push_to_hub:
      print("Pushing model to the hub..."   )

      model.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add model"							, use_temp_dir=lowerCamelCase							, )
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add image processor"							, use_temp_dir=lowerCamelCase							, )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			str			      =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )

