





"""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     __A      (			a_						:Any)		->		Optional[int]:
				if isinstance(a_   ,       collections.abc.Iterable):
								return x
				return (x, x)




@require_flax
class 				__lowercase			:







					'''simple docstring'''




					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									pass







					def  _lowerCamelCase		(						self     ):
									pass







					def  _lowerCamelCase		(						self     ):
									pass







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  np.abs((a - b)     ).max()
									self.assertLessEqual(_UpperCAmelCase	,			_UpperCAmelCase	,			f"""Difference between torch and flax is {diff} (>= {tol})."""     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						List[str]			       =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						Tuple			       =  FlaxVisionTextDualEncoderModel(_UpperCAmelCase     )

									__a :						int			       =  model(input_ids=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )

									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	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  self.get_vision_text_model(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						str			       =  {'''vision_model''': vision_model, '''text_model''': text_model}
									__a :						Optional[Any]			       =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase     )

									__a :						List[Any]			       =  model(input_ids=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )

									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	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						Optional[int]			       =  self.get_vision_text_model(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						int			       =  {'''vision_model''': vision_model, '''text_model''': text_model}
									__a :						Tuple			       =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase     )

									__a :						Optional[Any]			       =  model(input_ids=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )
									__a :						List[Any]			       =  output[0]

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(_UpperCAmelCase     )
													__a :						List[str]			       =  FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase     )

													__a :						str			       =  model(input_ids=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )
													__a :						Tuple			       =  after_output[0]
													__a :						List[Any]			       =  np.amax(np.abs(out_a - out_a     )     )
													self.assertLessEqual(_UpperCAmelCase	,			1e-3     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						str			       =  self.get_vision_text_model(_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Tuple			       =  {'''vision_model''': vision_model, '''text_model''': text_model}
									__a :						Any			       =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCAmelCase     )

									__a :						Any			       =  model(
									    input_ids=_UpperCAmelCase	,			pixel_values=_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			output_attentions=_UpperCAmelCase     )

									__a :						List[Any]			       =  output.vision_model_output.attentions
									self.assertEqual(len(_UpperCAmelCase     )	,			vision_config.num_hidden_layers     )

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

									__a :						Tuple			       =  output.text_model_output.attentions
									self.assertEqual(len(_UpperCAmelCase     )	,			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	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									pt_model.to(_UpperCAmelCase     )
									pt_model.eval()

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

									with torch.no_grad():
													__a :						Optional[int]			       =  pt_model(**_UpperCAmelCase     ).to_tuple()

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

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

									__a :						Optional[Any]			       =  fx_model_loaded(**_UpperCAmelCase     ).to_tuple()
									self.assertEqual(len(_UpperCAmelCase     )	,			len(_UpperCAmelCase     )	,			'''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(_UpperCAmelCase	,			pt_output.numpy()	,			4e-2     )

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

									pt_model_loaded.to(_UpperCAmelCase     )
									pt_model_loaded.eval()

									with torch.no_grad():
													__a :						Tuple			       =  pt_model_loaded(**_UpperCAmelCase     ).to_tuple()

									self.assertEqual(len(_UpperCAmelCase     )	,			len(_UpperCAmelCase     )	,			'''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(_UpperCAmelCase	,			pt_output_loaded.numpy()	,			4e-2     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  VisionTextDualEncoderModel(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  FlaxVisionTextDualEncoderModel(_UpperCAmelCase     )

									__a :						Tuple			       =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,			_UpperCAmelCase     )
									__a :						Tuple			       =  fx_state

									self.check_pt_flax_equivalence(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						str			       =  VisionTextDualEncoderModel(_UpperCAmelCase     )
									__a :						Tuple			       =  FlaxVisionTextDualEncoderModel(_UpperCAmelCase     )

									__a :						Any			       =  load_flax_weights_in_pytorch_model(_UpperCAmelCase	,			fx_model.params     )

									self.check_pt_flax_equivalence(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.prepare_config_and_inputs()
									self.check_save_load(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**_UpperCAmelCase     )







					@is_pt_flax_cross_test
					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.prepare_config_and_inputs()
									__a :						Tuple			       =  config_inputs_dict.pop('''vision_config'''     )
									__a :						Dict			       =  config_inputs_dict.pop('''text_config'''     )

									__a :						Optional[int]			       =  config_inputs_dict

									self.check_equivalence_pt_to_flax(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )
									self.check_equivalence_flax_to_pt(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )







					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  self.get_pretrained_model_and_inputs()

									__a :						Any			       =  model_a(**_UpperCAmelCase     )
									__a :						Any			       =  outputs[0]

									with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(_UpperCAmelCase     )
													__a :						Optional[int]			       =  FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCAmelCase     )

													__a :						Optional[Any]			       =  model_a(**_UpperCAmelCase     )
													__a :						str			       =  after_outputs[0]
													__a :						Optional[int]			       =  np.amax(np.abs(out_a - out_a     )     )
													self.assertLessEqual(_UpperCAmelCase	,			1e-5     )






@require_flax
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-vit'''	,			'''hf-internal-testing/tiny-bert'''	,			vision_from_pt=_UpperCAmelCase	,			text_from_pt=_UpperCAmelCase	,			)
									__a :						List[Any]			       =  13
									__a :						Union[str, Any]			       =  floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]     )
									__a :						int			       =  ids_tensor([batch_size, 4]	,			model.config.text_config.vocab_size     )
									__a :						List[str]			       =  random_attention_mask([batch_size, 4]     )
									__a :						List[Any]			       =  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						int			       =  FlaxViTModel(_UpperCAmelCase     )
									__a :						Any			       =  FlaxBertModel(_UpperCAmelCase     )
									return vision_model, text_model







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  FlaxViTModelTester(self     )
									__a :						Optional[int]			       =  FlaxBertModelTester(self     )
									__a :						Dict			       =  vit_model_tester.prepare_config_and_inputs()
									__a :						Dict			       =  bert_model_tester.prepare_config_and_inputs()

									__a :						Dict			       =  vision_config_and_inputs

									__a :						Union[str, Any]			       =  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 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    '''hf-internal-testing/tiny-random-clip'''	,			'''hf-internal-testing/tiny-bert'''	,			vision_from_pt=_UpperCAmelCase	,			text_from_pt=_UpperCAmelCase	,			)
									__a :						Optional[Any]			       =  13
									__a :						List[Any]			       =  floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]     )
									__a :						List[Any]			       =  ids_tensor([batch_size, 4]	,			model.config.text_config.vocab_size     )
									__a :						List[str]			       =  random_attention_mask([batch_size, 4]     )
									__a :						Any			       =  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

									return model, inputs







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  FlaxCLIPVisionModel(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  FlaxBertModel(_UpperCAmelCase     )
									return vision_model, text_model







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  FlaxCLIPVisionModelTester(self     )
									__a :						int			       =  FlaxBertModelTester(self     )
									__a :						List[Any]			       =  clip_model_tester.prepare_config_and_inputs()
									__a :						Optional[int]			       =  bert_model_tester.prepare_config_and_inputs()

									__a :						int			       =  vision_config_and_inputs

									__a :						Optional[Any]			       =  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 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''	,			logit_scale_init_value=1.0     )
									__a :						List[Any]			       =  VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''     )

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

									__a :						Optional[Any]			       =  model(**_UpperCAmelCase     )

									# 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 :						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	,			_UpperCAmelCase	,			atol=1e-3     )     )






"""simple docstring"""






def     __A      (			a_						:int = 2_00)		->		int:
				__a :						int			       =  [1, 2, 5, 10, 20, 50, 1_00, 2_00]
				__a :						List[Any]			       =  [0] * (pence + 1)
				__a :						Tuple			       =  1  # base case: 1 way to make 0 pence

				for coin in coins:
								for i in range(a_   ,       pence + 1   ,       1):
												number_of_ways[i] += number_of_ways[i - coin]
				return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73_682