



"""simple docstring"""
import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image



def        lowerCAmelCase_(   lowercase_     :							Optional[int]							)	->       Optional[int]:
				if isinstance(lowercase_						,   collections.abc.Iterable							):
								return x
				return (x, x)


@require_flax
class 							lowerCamelCase_:





					'''simple docstring'''



					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				np.abs((a - b)   ).max()
									self.assertLessEqual(lowerCamelCase__				,       lowerCamelCase__				,       F"""Difference between torch and flax is {diff} (>= {tol})."""   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel(lowerCamelCase__   )

									_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.get_vision_text_model(lowerCamelCase__				,       lowerCamelCase__   )
									_lowerCamelCase        =				{'''vision_model''': vision_model, '''text_model''': text_model}
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__   )

									_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.get_vision_text_model(lowerCamelCase__				,       lowerCamelCase__   )
									_lowerCamelCase        =				{'''vision_model''': vision_model, '''text_model''': text_model}
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__   )

									_lowerCamelCase        =				model(input_ids=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__   )
									_lowerCamelCase        =				output[0]

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__   )
													_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__   )

													_lowerCamelCase        =				model(input_ids=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__   )
													_lowerCamelCase        =				after_output[0]
													_lowerCamelCase        =				np.amax(np.abs(out_a - out_a   )   )
													self.assertLessEqual(lowerCamelCase__				,       1e-3   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.get_vision_text_model(lowerCamelCase__				,       lowerCamelCase__   )
									_lowerCamelCase        =				{'''vision_model''': vision_model, '''text_model''': text_model}
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__   )

									_lowerCamelCase        =				model(
									    input_ids=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       output_attentions=lowerCamelCase__   )

									_lowerCamelCase        =				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)
									_lowerCamelCase        =				to_atuple(vision_model.config.image_size   )
									_lowerCamelCase        =				to_atuple(vision_model.config.patch_size   )
									_lowerCamelCase        =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									_lowerCamelCase        =				num_patches + 1
									self.assertEqual(vision_attentions[0].shape[-3:]				,       (vision_config.num_attention_heads, seq_len, seq_len)   )

									_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									pt_model.to(lowerCamelCase__   )
									pt_model.eval()

									# prepare inputs
									_lowerCamelCase        =				inputs_dict
									_lowerCamelCase        =				{k: torch.tensor(v.tolist()   ) for k, v in flax_inputs.items()}

									with torch.no_grad():
													_lowerCamelCase        =				pt_model(**lowerCamelCase__   ).to_tuple()

									_lowerCamelCase        =				fx_model(**lowerCamelCase__   ).to_tuple()
									self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )				,       '''Output lengths differ between Flax and PyTorch'''   )
									for fx_output, pt_output in zip(fx_outputs[:4]				,       pt_outputs[:4]   ):
													self.assert_almost_equals(lowerCamelCase__				,       pt_output.numpy()				,       4e-2   )

									# PT -> Flax
									with tempfile.TemporaryDirectory() as tmpdirname:
													pt_model.save_pretrained(lowerCamelCase__   )
													_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__				,       from_pt=lowerCamelCase__   )

									_lowerCamelCase        =				fx_model_loaded(**lowerCamelCase__   ).to_tuple()
									self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )				,       '''Output lengths differ between Flax and PyTorch'''   )
									for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]				,       pt_outputs[:4]   ):
													self.assert_almost_equals(lowerCamelCase__				,       pt_output.numpy()				,       4e-2   )

									# Flax -> PT
									with tempfile.TemporaryDirectory() as tmpdirname:
													fx_model.save_pretrained(lowerCamelCase__   )
													_lowerCamelCase        =				VisionTextDualEncoderModel.from_pretrained(lowerCamelCase__				,       from_flax=lowerCamelCase__   )

									pt_model_loaded.to(lowerCamelCase__   )
									pt_model_loaded.eval()

									with torch.no_grad():
													_lowerCamelCase        =				pt_model_loaded(**lowerCamelCase__   ).to_tuple()

									self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )				,       '''Output lengths differ between Flax and PyTorch'''   )
									for fx_output, pt_output_loaded in zip(fx_outputs[:4]				,       pt_outputs_loaded[:4]   ):
													self.assert_almost_equals(lowerCamelCase__				,       pt_output_loaded.numpy()				,       4e-2   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				VisionTextDualEncoderModel(lowerCamelCase__   )
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel(lowerCamelCase__   )

									_lowerCamelCase        =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,       lowerCamelCase__   )
									_lowerCamelCase        =				fx_state

									self.check_pt_flax_equivalence(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__				,       lowerCamelCase__   )

									_lowerCamelCase        =				VisionTextDualEncoderModel(lowerCamelCase__   )
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel(lowerCamelCase__   )

									_lowerCamelCase        =				load_flax_weights_in_pytorch_model(lowerCamelCase__				,       fx_model.params   )

									self.check_pt_flax_equivalence(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									self.check_save_load(**lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**lowerCamelCase__   )

					@is_pt_flax_cross_test
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase        =				config_inputs_dict.pop('''vision_config'''   )
									_lowerCamelCase        =				config_inputs_dict.pop('''text_config'''   )

									_lowerCamelCase        =				config_inputs_dict

									self.check_equivalence_pt_to_flax(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
									self.check_equivalence_flax_to_pt(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.get_pretrained_model_and_inputs()

									_lowerCamelCase        =				model_a(**lowerCamelCase__   )
									_lowerCamelCase        =				outputs[0]

									with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(lowerCamelCase__   )
													_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__   )

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


@require_flax
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-vit'''				,       '''hf-internal-testing/tiny-bert'''				,       vision_from_pt=lowerCamelCase__				,       text_from_pt=lowerCamelCase__				,       )
									_lowerCamelCase        =				1_3
									_lowerCamelCase        =				floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]   )
									_lowerCamelCase        =				ids_tensor([batch_size, 4]				,       model.config.text_config.vocab_size   )
									_lowerCamelCase        =				random_attention_mask([batch_size, 4]   )
									_lowerCamelCase        =				{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				FlaxViTModel(lowerCamelCase__   )
									_lowerCamelCase        =				FlaxBertModel(lowerCamelCase__   )
									return vision_model, text_model





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxViTModelTester(self   )
									_lowerCamelCase        =				FlaxBertModelTester(self   )
									_lowerCamelCase        =				vit_model_tester.prepare_config_and_inputs()
									_lowerCamelCase        =				bert_model_tester.prepare_config_and_inputs()

									_lowerCamelCase  ,     _lowerCamelCase        =				vision_config_and_inputs

									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				text_config_and_inputs

									# make sure that cross attention layers are added
									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": attention_mask,
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									}


@require_torch
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-clip'''				,       '''hf-internal-testing/tiny-bert'''				,       vision_from_pt=lowerCamelCase__				,       text_from_pt=lowerCamelCase__				,       )
									_lowerCamelCase        =				1_3
									_lowerCamelCase        =				floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]   )
									_lowerCamelCase        =				ids_tensor([batch_size, 4]				,       model.config.text_config.vocab_size   )
									_lowerCamelCase        =				random_attention_mask([batch_size, 4]   )
									_lowerCamelCase        =				{'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				FlaxCLIPVisionModel(lowerCamelCase__   )
									_lowerCamelCase        =				FlaxBertModel(lowerCamelCase__   )
									return vision_model, text_model





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxCLIPVisionModelTester(self   )
									_lowerCamelCase        =				FlaxBertModelTester(self   )
									_lowerCamelCase        =				clip_model_tester.prepare_config_and_inputs()
									_lowerCamelCase        =				bert_model_tester.prepare_config_and_inputs()

									_lowerCamelCase  ,     _lowerCamelCase        =				vision_config_and_inputs

									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				text_config_and_inputs

									# make sure that cross attention layers are added
									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": attention_mask,
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									}


@require_flax
@require_vision
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''				,       logit_scale_init_value=1.0   )
									_lowerCamelCase        =				VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''   )

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

									_lowerCamelCase        =				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])				,       )

									_lowerCamelCase        =				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				,       lowerCamelCase__				,       atol=1e-3   )   )





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
