


'''simple docstring'''




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 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      int:




	"""simple docstring"""





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



@require_tf
class  lowerCAmelCase				:



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		pass



	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase					:    Tuple      							= VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    Any      							= TFVisionTextDualEncoderModel(snake_case__    )

		lowerCAmelCase					:    int      							= model(input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= self.get_vision_text_model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= TFVisionTextDualEncoderModel(vision_model=snake_case__ ,					text_model=snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= model(input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase							,   lowerCAmelCase					:    List[Any]      							= self.get_vision_text_model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    str      							= {'vision_model': vision_model, 'text_model': text_model}
		lowerCAmelCase					:    Optional[int]      							= TFVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__    )

		lowerCAmelCase					:    Optional[int]      							= model(input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Any      							= self.get_vision_text_model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Tuple      							= TFVisionTextDualEncoderModel(vision_model=snake_case__ ,					text_model=snake_case__    )

		lowerCAmelCase					:    str      							= model(input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__    )
		lowerCAmelCase					:    List[str]      							= output[0].numpy()

		with tempfile.TemporaryDirectory() as tmpdirname:
			model.save_pretrained(snake_case__    )
			lowerCAmelCase					:    Any      							= TFVisionTextDualEncoderModel.from_pretrained(snake_case__    )

			lowerCAmelCase					:    Union[str, Any]      							= model(input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__    )
			lowerCAmelCase					:    Union[str, Any]      							= after_output[0].numpy()
			lowerCAmelCase					:    str      							= np.amax(np.abs(out_a - out_a    )    )
			self.assertLessEqual(snake_case__ ,					1e-5    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= self.get_vision_text_model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    str      							= TFVisionTextDualEncoderModel(vision_model=snake_case__ ,					text_model=snake_case__    )

		lowerCAmelCase					:    List[Any]      							= model(
		    input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__ ,					output_attentions=snake_case__    )

		lowerCAmelCase					:    str      							= output.vision_model_output.attentions
		self.assertEqual(len(snake_case__    ) ,					vision_config.num_hidden_layers    )

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

		lowerCAmelCase					:    List[Any]      							= output.text_model_output.attentions
		self.assertEqual(len(snake_case__    ) ,					text_config.num_hidden_layers    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= np.abs((a - b)    ).max()
		self.assertLessEqual(snake_case__ ,					snake_case__ ,					f"Difference between torch and flax is {diff} (>= {tol})."    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_model(**snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.prepare_config_and_inputs()
		self.check_model_from_pretrained_configs(**snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_from_pretrained(**snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.prepare_config_and_inputs()
		self.check_save_load(**snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.prepare_config_and_inputs()
		self.check_vision_text_output_attention(**snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= self.get_pretrained_model_and_inputs()

		lowerCAmelCase					:    Dict      							= model_a(**snake_case__    )
		lowerCAmelCase					:    List[str]      							= outputs[0].numpy()

		with tempfile.TemporaryDirectory() as tmp_dirname:
			model_a.save_pretrained(snake_case__    )
			lowerCAmelCase					:    Dict      							= TFVisionTextDualEncoderModel.from_pretrained(snake_case__    )

			lowerCAmelCase					:    Dict      							= model_a(**snake_case__    )
			lowerCAmelCase					:    int      							= after_outputs[0].numpy()
			lowerCAmelCase					:    Union[str, Any]      							= np.amax(np.abs(out_a - out_a    )    )
			self.assertLessEqual(snake_case__ ,					1e-5    )



@require_tf
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
		    'hf-internal-testing/tiny-random-vit' ,					'hf-internal-testing/tiny-random-bert'    )
		lowerCAmelCase					:    Dict      							= 13
		lowerCAmelCase					:    List[str]      							= floats_tensor(
		    [
		        batch_size,
		        model.vision_model.config.num_channels,
		        model.vision_model.config.image_size,
		        model.vision_model.config.image_size,
		    ]    )
		lowerCAmelCase					:    Optional[Any]      							= ids_tensor([batch_size, 4] ,					model.text_model.config.vocab_size    )
		lowerCAmelCase					:    Union[str, Any]      							= random_attention_mask([batch_size, 4]    )
		lowerCAmelCase					:    List[Any]      							= {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

		return model, inputs



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= TFViTModel(snake_case__ ,					name='vision_model'    )
		lowerCAmelCase					:    Dict      							= TFBertModel(snake_case__ ,					name='text_model'    )
		return vision_model, text_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= TFViTModelTester(self    )
		lowerCAmelCase					:    Any      							= TFBertModelTester(self    )
		lowerCAmelCase					:    Any      							= vit_model_tester.prepare_config_and_inputs()
		lowerCAmelCase					:    Optional[Any]      							= bert_model_tester.prepare_config_and_inputs()

		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= vision_config_and_inputs

		(
		    (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   
		)					:    Tuple      							= text_config_and_inputs

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



@require_tf
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):



	def 				lowercase     (					self    ):
		# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
		# just reinitialize it.
		lowerCAmelCase					:    List[str]      							= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
		    'Rocketknight1/tiny-random-deit-tf' ,					'hf-internal-testing/tiny-random-roberta'    )
		lowerCAmelCase					:    Any      							= 13
		lowerCAmelCase					:    Union[str, Any]      							= floats_tensor(
		    [
		        batch_size,
		        model.vision_model.config.num_channels,
		        model.vision_model.config.image_size,
		        model.vision_model.config.image_size,
		    ]    )
		lowerCAmelCase					:    str      							= ids_tensor([batch_size, 4] ,					model.text_model.config.vocab_size    )
		lowerCAmelCase					:    Any      							= random_attention_mask([batch_size, 4]    )
		lowerCAmelCase					:    str      							= {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

		return model, inputs



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					**snake_case__    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[int]      							= self.get_vision_text_model(snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    str      							= TFVisionTextDualEncoderModel(vision_model=snake_case__ ,					text_model=snake_case__    )

		lowerCAmelCase					:    Optional[int]      							= model(
		    input_ids=snake_case__ ,					pixel_values=snake_case__ ,					attention_mask=snake_case__ ,					output_attentions=snake_case__    )

		lowerCAmelCase					:    str      							= output.vision_model_output.attentions
		self.assertEqual(len(snake_case__    ) ,					vision_config.num_hidden_layers    )

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

		lowerCAmelCase					:    Tuple      							= output.text_model_output.attentions
		self.assertEqual(len(snake_case__    ) ,					text_config.num_hidden_layers    )

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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= TFDeiTModel(snake_case__ ,					name='vision_model'    )
		lowerCAmelCase					:    List[str]      							= TFRobertaModel(snake_case__ ,					name='text_model'    )
		return vision_model, text_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= TFDeiTModelTester(self    )
		lowerCAmelCase					:    int      							= TFRobertaModelTester(self    )
		lowerCAmelCase					:    List[str]      							= vit_model_tester.prepare_config_and_inputs()
		lowerCAmelCase					:    Any      							= bert_model_tester.prepare_config_and_inputs()

		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Any      							= vision_config_and_inputs

		(
		    (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   
		)					:    List[Any]      							= text_config_and_inputs

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



@require_tf
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
		    'Rocketknight1/tiny-random-clip-tf' ,					'hf-internal-testing/tiny-random-bert'    )
		lowerCAmelCase					:    List[Any]      							= 13
		lowerCAmelCase					:    Union[str, Any]      							= floats_tensor(
		    [
		        batch_size,
		        model.vision_model.config.num_channels,
		        model.vision_model.config.image_size,
		        model.vision_model.config.image_size,
		    ]    )
		lowerCAmelCase					:    Any      							= ids_tensor([batch_size, 4] ,					model.text_model.config.vocab_size    )
		lowerCAmelCase					:    List[Any]      							= random_attention_mask([batch_size, 4]    )
		lowerCAmelCase					:    str      							= {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

		return model, inputs



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= TFCLIPVisionModel(snake_case__ ,					name='vision_model'    )
		lowerCAmelCase					:    List[str]      							= TFBertModel(snake_case__ ,					name='text_model'    )
		return vision_model, text_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= TFCLIPVisionModelTester(self    )
		lowerCAmelCase					:    List[str]      							= TFBertModelTester(self    )
		lowerCAmelCase					:    int      							= clip_model_tester.prepare_config_and_inputs()
		lowerCAmelCase					:    Optional[int]      							= bert_model_tester.prepare_config_and_inputs()

		lowerCAmelCase							,   lowerCAmelCase					:    Optional[int]      							= vision_config_and_inputs

		(
		    (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   
		)					:    List[str]      							= text_config_and_inputs

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



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



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= TFVisionTextDualEncoderModel.from_pretrained(
		    'clip-italian/clip-italian' ,					logit_scale_init_value=1.0 ,					from_pt=snake_case__    )
		lowerCAmelCase					:    Tuple      							= VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'    )

		lowerCAmelCase					:    Union[str, Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
		lowerCAmelCase					:    Tuple      							= processor(
		    text=['una foto di un gatto', 'una foto di un cane'] ,					images=snake_case__ ,					padding=snake_case__ ,					return_tensors='np'    )

		lowerCAmelCase					:    List[str]      							= model(**snake_case__    )

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

		lowerCAmelCase					:    List[Any]      							= np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]    )

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




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Optional[Any]:




	"""simple docstring"""





	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase							,   lowerCAmelCase					:    str      							= head.next, head
	while fast and fast.next:
		lowerCAmelCase					:    Optional[int]      							= fast.next.next
		lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    int      							= slow.next
	lowerCAmelCase					:    Optional[Any]      							= None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase					:    List[Any]      							= None
	while second:
		lowerCAmelCase					:    List[Any]      							= second.next
		lowerCAmelCase					:    Union[str, Any]      							= node
		lowerCAmelCase					:    Optional[Any]      							= second
		lowerCAmelCase					:    Any      							= nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase					:    Optional[Any]      							= node.next
		lowerCAmelCase					:    Tuple      							= head.next
	return True

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase					:    Optional[int]      							= head
	while fast and fast.next:
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase					:    Tuple      							= [slow.val]
	while slow.next:
		lowerCAmelCase					:    Tuple      							= slow.next
		stack.append(slow.val				)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase					:    Union[str, Any]      							= cur.next

	return True






def 			__UpperCamelCase    (    _A    :				Tuple				)    ->      Optional[int]:




	"""simple docstring"""





	if not head or not head.next:
		return True
	lowerCAmelCase					:    Optional[int]      							= {}
	lowerCAmelCase					:    int      							= 0
	while head:
		if head.val in d:
			d[head.val].append(_A				)
		else:
			lowerCAmelCase					:    Any      							= [pos]
		lowerCAmelCase					:    int      							= head.next
		pos += 1
	lowerCAmelCase					:    str      							= pos - 1
	lowerCAmelCase					:    Optional[Any]      							= 0
	for v in d.values():
		if len(_A				) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase					:    Any      							= 0
			for i in range(0					,   len(_A				)				):
				if v[i] + v[len(_A				) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True
