



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

@require_tf
class     _A       :



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a						       =				VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				TFVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE)

           __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_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 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE     ,		text_model=__SCREAMING_SNAKE_CASE)

           __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_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 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{'''vision_model''': vision_model, '''text_model''': text_model}
           __a						       =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE)

           __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_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 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE     ,		text_model=__SCREAMING_SNAKE_CASE)

           __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)
           __a						       =				output[0].numpy()

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a						       =				TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)
               __a						       =				after_output[0].numpy()
               __a						       =				np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,		1E-5)



       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=None     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE     ,		text_model=__SCREAMING_SNAKE_CASE)

           __a						       =				model(
               input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a						       =				output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		vision_config.num_hidden_layers)

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

           __a						       =				output.text_model_output.attentions
           self.assertEqual(len(__SCREAMING_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 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     float):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_model(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           self.check_save_load(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_pretrained_model_and_inputs()

           __a						       =				model_a(**__SCREAMING_SNAKE_CASE)
           __a						       =				outputs[0].numpy()

           with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a						       =				TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a						       =				model_a(**__SCREAMING_SNAKE_CASE)
               __a						       =				after_outputs[0].numpy()
               __a						       =				np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,		1E-5)


@require_tf
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

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

           return model, inputs



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           __a						       =				TFViTModel(__SCREAMING_SNAKE_CASE     ,		name='''vision_model''')
           __a						       =				TFBertModel(__SCREAMING_SNAKE_CASE     ,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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

           __a       ,   __a       ,   __a						       =				vision_config_and_inputs

           (
               (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   
           )						       =				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     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

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

           return model, inputs



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[str]=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           __a       ,   __a						       =				self.get_vision_text_model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE     ,		text_model=__SCREAMING_SNAKE_CASE)

           __a						       =				model(
               input_ids=__SCREAMING_SNAKE_CASE     ,		pixel_values=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a						       =				output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		vision_config.num_hidden_layers)

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

           __a						       =				output.text_model_output.attentions
           self.assertEqual(len(__SCREAMING_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 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           __a						       =				TFDeiTModel(__SCREAMING_SNAKE_CASE     ,		name='''vision_model''')
           __a						       =				TFRobertaModel(__SCREAMING_SNAKE_CASE     ,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

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

           __a       ,   __a       ,   __a						       =				vision_config_and_inputs

           (
               (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   
           )						       =				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     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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

           return model, inputs



       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				TFCLIPVisionModel(__SCREAMING_SNAKE_CASE     ,		name='''vision_model''')
           __a						       =				TFBertModel(__SCREAMING_SNAKE_CASE     ,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

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

           __a       ,   __a						       =				vision_config_and_inputs

           (
               (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   (
               __a
           )       ,   
           )						       =				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     _A       (	unittest.TestCase    ):



       @slow
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				TFVisionTextDualEncoderModel.from_pretrained(
               '''clip-italian/clip-italian'''     ,		logit_scale_init_value=1.0     ,		from_pt=__SCREAMING_SNAKE_CASE)
           __a						       =				VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

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

           __a						       =				model(**__SCREAMING_SNAKE_CASE)

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

           __a						       =				np.array([[1.2_28_47_27, 0.3_10_41_22]])

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







def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


