






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCamelCase      :      Any     						=						16
__UpperCamelCase      :      Tuple     						=						32





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = 16				,  lowerCamelCase = "bert-base-cased"					):




	'''simple docstring'''







	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	__lowercase             =  load_dataset("""glue"""				,  """mrpc"""					)

	def tokenize_function(lowerCamelCase					):
		# max_length=None => use the model max length (it's actually the default)
		__lowercase             =  tokenizer(examples["""sentence1"""]				,  examples["""sentence2"""]				,  truncation=lowerCamelCase				,  max_length=lowerCamelCase					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	__lowercase             =  datasets.map(
	    lowerCamelCase				,  batched=lowerCamelCase				,  remove_columns=["""idx""", """sentence1""", """sentence2"""]				,  load_from_cache_file=lowerCamelCase					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	__lowercase             =  tokenized_datasets.rename_column("""label"""				,  """labels"""					)

	def collate_fn(lowerCamelCase					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(lowerCamelCase				,  padding="""max_length"""				,  max_length=128				,  return_tensors="""pt"""					)
		return tokenizer.pad(lowerCamelCase				,  padding="""longest"""				,  return_tensors="""pt"""					)

	# Instantiate dataloaders.
	__lowercase             =  DataLoader(
	    tokenized_datasets["""train"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)
	__lowercase             =  DataLoader(
	    tokenized_datasets["""validation"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)

	return train_dataloader, eval_dataloader





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	model.eval()
	__lowercase             =  0
	for step, batch in enumerate(lowerCamelCase					):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device					)
		with torch.no_grad():
			__lowercase             =  model(**lowerCamelCase					)
		__lowercase             =  outputs.logits.argmax(dim=-1					)
		# It is slightly faster to call this once, than multiple times
		__lowercase       ,						__lowercase             =  accelerator.gather(
		    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(lowerCamelCase					) - 1:
				__lowercase             =  predictions[: len(eval_dataloader.dataset					) - samples_seen]
				__lowercase             =  references[: len(eval_dataloader.dataset					) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=lowerCamelCase				,  references=lowerCamelCase				,  )

	__lowercase             =  metric.compute()
	return eval_metric["accuracy"]





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowercase             =  config["""lr"""]
	__lowercase             =  int(config["""num_epochs"""]					)
	__lowercase             =  int(config["""seed"""]					)
	__lowercase             =  int(config["""batch_size"""]					)
	__lowercase             =  args.model_name_or_path

	set_seed(lowerCamelCase					)
	__lowercase       ,						__lowercase             =  get_dataloaders(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase             =  AutoModelForSequenceClassification.from_pretrained(lowerCamelCase				,  return_dict=lowerCamelCase					)

	# Instantiate optimizer
	__lowercase             =  (
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	__lowercase             =  optimizer_cls(params=model.parameters()				,  lr=lowerCamelCase					)

	if accelerator.state.deepspeed_plugin is not None:
		__lowercase             =  accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		__lowercase             =  1
	__lowercase             =  (len(lowerCamelCase					) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		__lowercase             =  get_linear_schedule_with_warmup(
		    optimizer=lowerCamelCase				,  num_warmup_steps=0				,  num_training_steps=lowerCamelCase				,  )
	else:
		__lowercase             =  DummyScheduler(lowerCamelCase				,  total_num_steps=lowerCamelCase				,  warmup_num_steps=0					)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  accelerator.prepare(
	    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	# We need to keep track of how many total steps we have iterated over
	__lowercase             =  0
	# We also need to keep track of the stating epoch so files are named properly
	__lowercase             =  0
	__lowercase             =  evaluate.load("""glue"""				,  """mrpc"""					)
	__lowercase             =  num_epochs

	if args.partial_train_epoch is not None:
		__lowercase             =  args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint					)
		__lowercase             =  args.resume_from_checkpoint.split("""epoch_"""					)[1]
		__lowercase             =  """"""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		__lowercase             =  int(lowerCamelCase					) + 1
		__lowercase             =  evaluation_loop(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
		accelerator.print("""resumed checkpoint performance:"""				,  lowerCamelCase					)
		accelerator.print("""resumed checkpoint's scheduler's lr:"""				,  lr_scheduler.get_lr()[0]					)
		accelerator.print("""resumed optimizers's lr:"""				,  optimizer.param_groups[0]["""lr"""]					)
		with open(os.path.join(args.output_dir				,  F'state_{starting_epoch-1}.json'					)				,  """r"""					) as f:
			__lowercase             =  json.load(lowerCamelCase					)
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	__lowercase             =  {}
	for epoch in range(lowerCamelCase				,  lowerCamelCase					):
		model.train()
		for step, batch in enumerate(lowerCamelCase					):
			__lowercase             =  model(**lowerCamelCase					)
			__lowercase             =  outputs.loss
			__lowercase             =  loss / gradient_accumulation_steps
			accelerator.backward(lowerCamelCase					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		__lowercase             =  F'epoch_{epoch}'
		__lowercase             =  os.path.join(args.output_dir				,  lowerCamelCase					)
		accelerator.save_state(lowerCamelCase					)
		__lowercase             =  evaluation_loop(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
		__lowercase             =  accuracy
		__lowercase             =  lr_scheduler.get_lr()[0]
		__lowercase             =  optimizer.param_groups[0]["""lr"""]
		__lowercase             =  epoch
		__lowercase             =  overall_step
		accelerator.print(F'epoch {epoch}:'				,  lowerCamelCase					)

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir				,  F'state_{epoch}.json'					)				,  """w"""					) as f:
				json.dump(lowerCamelCase				,  lowerCamelCase					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""				,  type=lowerCamelCase				,  default="""bert-base-cased"""				,  help="""Path to pretrained model or model identifier from huggingface.co/models."""				,  required=lowerCamelCase				,  )
	parser.add_argument(
	    """--output_dir"""				,  type=lowerCamelCase				,  default="""."""				,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,  )
	parser.add_argument(
	    """--resume_from_checkpoint"""				,  type=lowerCamelCase				,  default=lowerCamelCase				,  help="""If the training should continue from a checkpoint folder."""				,  )
	parser.add_argument(
	    """--partial_train_epoch"""				,  type=lowerCamelCase				,  default=lowerCamelCase				,  help="""If passed, the training will stop after this number of epochs."""				,  )
	parser.add_argument(
	    """--num_epochs"""				,  type=lowerCamelCase				,  default=2				,  help="""Number of train epochs."""				,  )
	__lowercase             =  parser.parse_args()
	__lowercase             =  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

	training_function(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						main()









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			)			)

