






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      Tuple     						=						logging.get_logger()





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = True					):




	'''simple docstring'''







	print(F'Converting {name}...'					)

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				__lowercase             =  timm.create_model("""levit_128s"""				,  pretrained=lowerCamelCase					)
			else:
				__lowercase             =  timm.create_model("""levit_128"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 192:
			__lowercase             =  timm.create_model("""levit_192"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 256:
			__lowercase             =  timm.create_model("""levit_256"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 384:
			__lowercase             =  timm.create_model("""levit_384"""				,  pretrained=lowerCamelCase					)

		from_model.eval()
		__lowercase             =  LevitForImageClassificationWithTeacher(lowerCamelCase					).eval()
		__lowercase             =  OrderedDict()

		__lowercase             =  from_model.state_dict()
		__lowercase             =  list(from_model.state_dict().keys()					)
		__lowercase             =  list(our_model.state_dict().keys()					)
		print(len(lowerCamelCase					)				,  len(lowerCamelCase					)					)
		for i in range(len(lowerCamelCase					)					):
			__lowercase             =  weights[og_keys[i]]
		our_model.load_state_dict(lowerCamelCase					)

		__lowercase             =  torch.randn((2, 3, 224, 224)					)
		__lowercase             =  from_model(lowerCamelCase					)
		__lowercase             =  our_model(lowerCamelCase					).logits

	assert torch.allclose(lowerCamelCase				,  lowerCamelCase					), "The model logits don't match the original one."

	__lowercase             =  name
	print(lowerCamelCase					)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name					)
		__lowercase             =  LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name					)

		print(F'Pushed {checkpoint_name}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = None				,  lowerCamelCase = True					):




	'''simple docstring'''







	__lowercase             =  """imagenet-1k-id2label.json"""
	__lowercase             =  1_000
	__lowercase             =  (1, num_labels)

	__lowercase             =  """huggingface/label-files"""
	__lowercase             =  num_labels
	__lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
	__lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}

	__lowercase             =  idalabel
	__lowercase             =  {v: k for k, v in idalabel.items()}

	__lowercase             =  partial(lowerCamelCase				,  num_labels=lowerCamelCase				,  idalabel=lowerCamelCase				,  labelaid=lowerCamelCase					)

	__lowercase             =  {
	    """levit-128S""": 128,
	    """levit-128""": 128,
	    """levit-192""": 192,
	    """levit-256""": 256,
	    """levit-384""": 384,
	}

	__lowercase             =  {
	    """levit-128S""": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,  num_attention_heads=[4, 6, 8]				,  depths=[2, 3, 4]				,  key_dim=[16, 16, 16]				,  drop_path_rate=0				,  ),
	    """levit-128""": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,  num_attention_heads=[4, 8, 12]				,  depths=[4, 4, 4]				,  key_dim=[16, 16, 16]				,  drop_path_rate=0				,  ),
	    """levit-192""": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384]				,  num_attention_heads=[3, 5, 6]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0				,  ),
	    """levit-256""": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512]				,  num_attention_heads=[4, 6, 8]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0				,  ),
	    """levit-384""": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768]				,  num_attention_heads=[6, 9, 12]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0.1				,  ),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]				,  lowerCamelCase				,  names_to_config[model_name]				,  lowerCamelCase				,  lowerCamelCase					)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
	return config, expected_shape


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default=None,
						    type=str,
						    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""levit-dump-folder/""",
						    type=Path,
						    required=False,
						    help="""Path to the output PyTorch model directory.""",
						)
						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""",
						)

						__UpperCamelCase      :      List[str]     						=						parser.parse_args()
						__UpperCamelCase      :      Path     						=						args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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


@require_tf
class        __UpperCamelCase    :







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict=None       ,				**_lowerCAmelCase      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase       ,				_lowerCAmelCase			)

		__lowercase             =  TFVisionTextDualEncoderModel(_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							str       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int=None       ,				**_lowerCAmelCase      :							Dict			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Any=None       ,				**_lowerCAmelCase      :							str			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  {"""vision_model""": vision_model, """text_model""": text_model}
		__lowercase             =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							List[Any]=None       ,				**_lowerCAmelCase      :							Any			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)
		__lowercase             =  output[0].numpy()

		with tempfile.TemporaryDirectory() as tmpdirname:
			model.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase			)

			__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)
			__lowercase             =  after_output[0].numpy()
			__lowercase             =  np.amax(np.abs(out_a - out_a			)			)
			self.assertLessEqual(_lowerCAmelCase       ,				1e-5			)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[int]=None       ,				**_lowerCAmelCase      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(
		    input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				output_attentions=_lowerCAmelCase			)

		__lowercase             =  output.vision_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				vision_config.num_hidden_layers			)

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

		__lowercase             =  output.text_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							float			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  np.abs((a - b)			).max()
		self.assertLessEqual(_lowerCAmelCase       ,				_lowerCAmelCase       ,				F'Difference between torch and flax is {diff} (>= {tol}).'			)







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







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_model(**_lowerCAmelCase			)







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







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_model_from_pretrained_configs(**_lowerCAmelCase			)







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







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase			)







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







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_save_load(**_lowerCAmelCase			)







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







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_output_attention(**_lowerCAmelCase			)







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







		__lowercase       ,						__lowercase             =  self.get_pretrained_model_and_inputs()

		__lowercase             =  model_a(**_lowerCAmelCase			)
		__lowercase             =  outputs[0].numpy()

		with tempfile.TemporaryDirectory() as tmp_dirname:
			model_a.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase			)

			__lowercase             =  model_a(**_lowerCAmelCase			)
			__lowercase             =  after_outputs[0].numpy()
			__lowercase             =  np.amax(np.abs(out_a - out_a			)			)
			self.assertLessEqual(_lowerCAmelCase       ,				1e-5			)


@require_tf
class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







	def  _a   (  self      :							Optional[int]			)		-> List[str]:
		"""simple docstring"""







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

		return model, inputs







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  TFViTModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFBertModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  TFViTModelTester(self			)
		__lowercase             =  TFBertModelTester(self			)
		__lowercase             =  vit_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







	def  _a   (  self      :							Tuple			)		-> Any:
		"""simple docstring"""







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

		return model, inputs







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int=None       ,				**_lowerCAmelCase      :							Tuple			)		-> Dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(
		    input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				output_attentions=_lowerCAmelCase			)

		__lowercase             =  output.vision_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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)
		__lowercase             =  to_atuple(vision_model.config.image_size			)
		__lowercase             =  to_atuple(vision_model.config.patch_size			)
		__lowercase             =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
		__lowercase             =  num_patches + 2
		self.assertEqual(vision_attentions[0].shape[-3:]       ,				(vision_config.num_attention_heads, seq_len, seq_len)			)

		__lowercase             =  output.text_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict			)		-> int:
		"""simple docstring"""







		__lowercase             =  TFDeiTModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFRobertaModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase             =  TFDeiTModelTester(self			)
		__lowercase             =  TFRobertaModelTester(self			)
		__lowercase             =  vit_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







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







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

		return model, inputs







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  TFCLIPVisionModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFBertModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







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







		__lowercase             =  TFCLIPVisionModelTester(self			)
		__lowercase             =  TFBertModelTester(self			)
		__lowercase             =  clip_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     unittest.TestCase   ):







	@slow
	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(
		    """clip-italian/clip-italian"""       ,				logit_scale_init_value=1.0       ,				from_pt=_lowerCAmelCase			)
		__lowercase             =  VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""			)

		__lowercase             =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
		__lowercase             =  processor(
		    text=["""una foto di un gatto""", """una foto di un cane"""]       ,				images=_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				return_tensors="""np"""			)

		__lowercase             =  model(**_lowerCAmelCase			)

		# 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])       ,				)

		__lowercase             =  np.array([[1.2_284_727, 0.3_104_122]]			)

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

