






from math import sqrt





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    number >= 0
	), "'number' must been an int and positive"

	__lowercase             =  True

	# 0 and 1 are none primes.
	if number <= 1:
		__lowercase             =  False

	for divisor in range(2				,  int(round(sqrt(lowerCamelCase					)					)					) + 1					):
		# if 'number' divisible by 'divisor' then sets 'status'
		# of false and break up the loop.
		if number % divisor == 0:
			__lowercase             =  False
			break

    # precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'status' must been from type bool"

	return status





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n > 2), "'N' must been an int and > 2"

	# beginList: contains all natural numbers from 2 up to N
	__lowercase             =  list(range(2				,  n + 1					)					)

	__lowercase             =  []  # this list will be returns.

	# actual sieve of erathostenes
	for i in range(len(lowerCamelCase					)					):
		for j in range(i + 1				,  len(lowerCamelCase					)					):
			if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
				__lowercase             =  0

    # filters actual prime numbers.
	__lowercase             =  [x for x in begin_list if x != 0]

	# precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'ans' must been from type list"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n > 2), "'N' must been an int and > 2"

	__lowercase             =  []

	# iterates over all numbers between 2 up to N+1
	# if a number is prime then appends to list 'ans'
	for number in range(2				,  n + 1					):
		if is_prime(lowerCamelCase					):
			ans.append(lowerCamelCase					)

    # precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'ans' must been from type list"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and number >= 0, "'number' must been an int and >= 0"

	__lowercase             =  []  # this list will be returns of the function.

	# potential prime number factors.

	__lowercase             =  2

	__lowercase             =  number

	if number == 0 or number == 1:
		ans.append(lowerCamelCase					)

	# if 'number' not prime then builds the prime factorization of 'number'
	elif not is_prime(lowerCamelCase					):
		while quotient != 1:
			if is_prime(lowerCamelCase					) and (quotient % factor == 0):
				ans.append(lowerCamelCase					)
				quotient /= factor
			else:
				factor += 1

	else:
		ans.append(lowerCamelCase					)

	# precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'ans' must been from type list"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    number >= 0
	), "'number' bust been an int and >= 0"

	__lowercase             =  0

	# prime factorization of 'number'
	__lowercase             =  prime_factorization(lowerCamelCase					)

	__lowercase             =  max(lowerCamelCase					)

	# precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'ans' must been from type int"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    number >= 0
	), "'number' bust been an int and >= 0"

	__lowercase             =  0

	# prime factorization of 'number'
	__lowercase             =  prime_factorization(lowerCamelCase					)

	__lowercase             =  min(lowerCamelCase					)

	# precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'ans' must been from type int"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'number' must been an int"
	assert isinstance(number % 2 == 0				,  lowerCamelCase					), "compare bust been from type bool"

	return number % 2 == 0





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					), "'number' must been an int"
	assert isinstance(number % 2 != 0				,  lowerCamelCase					), "compare bust been from type bool"

	return number % 2 != 0





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					) and (number > 2) and is_even(lowerCamelCase					)
	), "'number' must been an int, even and > 2"

	__lowercase             =  []  # this list will returned

	# creates a list of prime numbers between 2 up to 'number'
	__lowercase             =  get_prime_numbers(lowerCamelCase					)
	__lowercase             =  len(lowerCamelCase					)

	# run variable for while-loops.
	__lowercase             =  0
	__lowercase             =  None

	# exit variable. for break up the loops
	__lowercase             =  True

	while i < len_pn and loop:
		__lowercase             =  i + 1

		while j < len_pn and loop:
			if prime_numbers[i] + prime_numbers[j] == number:
				__lowercase             =  False
				ans.append(prime_numbers[i]					)
				ans.append(prime_numbers[j]					)

			j += 1

		i += 1

	# precondition
	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (len(lowerCamelCase					) == 2)
	    and (ans[0] + ans[1] == number)
	    and is_prime(ans[0]					)
	    and is_prime(ans[1]					)
	), "'ans' must contains two primes. And sum of elements must been eq 'number'"

	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (numbera >= 0)
	    and (numbera >= 0)
	), "'number1' and 'number2' must been positive integer."

	__lowercase             =  0

	while numbera != 0:
		__lowercase             =  numbera % numbera
		__lowercase             =  numbera
		__lowercase             =  rest

	# precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    numbera >= 0
	), "'number' must been from type int and positive"

	return numbera





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (numbera >= 1)
	    and (numbera >= 1)
	), "'number1' and 'number2' must been positive integer."

	__lowercase             =  1  # actual answer that will be return.

	# for kgV (x,1)
	if numbera > 1 and numbera > 1:
		# builds the prime factorization of 'number1' and 'number2'
		__lowercase             =  prime_factorization(lowerCamelCase					)
		__lowercase             =  prime_factorization(lowerCamelCase					)

	elif numbera == 1 or numbera == 1:
		__lowercase             =  []
		__lowercase             =  []
		__lowercase             =  max(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  0
	__lowercase             =  0

	__lowercase             =  []  # captured numbers int both 'primeFac1' and 'primeFac2'

	# iterates through primeFac1
	for n in prime_fac_a:
		if n not in done:
			if n in prime_fac_a:
				__lowercase             =  prime_fac_a.count(lowerCamelCase					)
				__lowercase             =  prime_fac_a.count(lowerCamelCase					)

				for _ in range(max(lowerCamelCase				,  lowerCamelCase					)					):
					ans *= n

			else:
				__lowercase             =  prime_fac_a.count(lowerCamelCase					)

				for _ in range(lowerCamelCase					):
					ans *= n

			done.append(lowerCamelCase					)

    # iterates through primeFac2
	for n in prime_fac_a:
		if n not in done:
			__lowercase             =  prime_fac_a.count(lowerCamelCase					)

			for _ in range(lowerCamelCase					):
				ans *= n

			done.append(lowerCamelCase					)

    # precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    ans >= 0
	), "'ans' must been from type int and positive"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n >= 0), "'number' must been a positive int"

	__lowercase             =  0
	__lowercase             =  2  # this variable holds the answer

	while index < n:
		index += 1

		ans += 1  # counts to the next number

		# if ans not prime then
		# runs to the next prime number.
		while not is_prime(lowerCamelCase					):
			ans += 1

    # precondition
	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and is_prime(
	    lowerCamelCase					), "'ans' must been a prime number and from type int"

	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	assert (
	    is_prime(lowerCamelCase					) and is_prime(lowerCamelCase					) and (p_number_a < p_number_a)
	), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

	__lowercase             =  p_number_a + 1  # jump to the next number

	__lowercase             =  []  # this list will be returns.

	# if number is not prime then
	# fetch the next prime number.
	while not is_prime(lowerCamelCase					):
		number += 1

	while number < p_number_a:
		ans.append(lowerCamelCase					)

		number += 1

		# fetch the next prime number.
		while not is_prime(lowerCamelCase					):
			number += 1

    # precondition
	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and ans[0] != p_number_a
	    and ans[len(lowerCamelCase					) - 1] != p_number_a
	), "'ans' must been a list without the arguments"

	# 'ans' contains not 'pNumber1' and 'pNumber2' !
	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n >= 1), "'n' must been int and >= 1"

	__lowercase             =  []  # will be returned.

	for divisor in range(1				,  n + 1					):
		if n % divisor == 0:
			ans.append(lowerCamelCase					)

    # precondition
	assert ans[0] == 1 and ans[len(lowerCamelCase					) - 1] == n, "Error in function getDivisiors(...)"

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (
	    number > 1
	), "'number' must been an int and >= 1"

	__lowercase             =  get_divisors(lowerCamelCase					)

	# precondition
	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (divisors[0] == 1)
	    and (divisors[len(lowerCamelCase					) - 1] == number)
	), "Error in help-function getDivisiors(...)"

	# summed all divisors up to 'number' (exclusive), hence [:-1]
	return sum(divisors[:-1]					) == number





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (denominator != 0)
	), "The arguments must been from type int and 'denominator' != 0"

	# build the greatest common divisor of numerator and denominator.
	__lowercase             =  gcd(abs(lowerCamelCase					)				,  abs(lowerCamelCase					)					)

	# precondition
	assert (
	    isinstance(lowerCamelCase				,  lowerCamelCase					)
	    and (numerator % gcd_of_fraction == 0)
	    and (denominator % gcd_of_fraction == 0)
	), "Error in function gcd(...,...)"

	return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n >= 0), "'n' must been a int and >= 0"

	__lowercase             =  1  # this will be return.

	for factor in range(1				,  n + 1					):
		ans *= factor

	return ans





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	assert isinstance(lowerCamelCase				,  lowerCamelCase					) and (n >= 0), "'n' must been an int and >= 0"

	__lowercase             =  0
	__lowercase             =  1
	__lowercase             =  1  # this will be return

	for _ in range(n - 1					):
		__lowercase             =  ans
		ans += fiba
		__lowercase             =  tmp

	return ans









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			)			)

