



'''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__       (	UpperCAmelCase_				):
						if isinstance(UpperCAmelCase_    ,    collections.abc.Iterable				):
												return x
						return (x, x)






@require_flax
class 				lowercase__      :





	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Any					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							float					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  np.abs((a - b)					).max()
							self.assertLessEqual(lowerCamelCase__					,lowerCamelCase__					,F'Difference between torch and flax is {diff} (>= {tol}).'					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							List[str]=None					,**lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__					,lowerCamelCase__					)

							_UpperCamelCase   :						int												=  FlaxVisionTextDualEncoderModel(lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, Any]												=  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       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Optional[int]=None					,**lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						int												=  {'vision_model': vision_model, 'text_model': text_model}
							_UpperCamelCase   :						Optional[Any]												=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__					)

							_UpperCamelCase   :						str												=  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       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str=None					,**lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						str												=  self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  {'vision_model': vision_model, 'text_model': text_model}
							_UpperCamelCase   :						Tuple												=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  output[0]

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

													_UpperCamelCase   :						int												=  model(input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__					)
													_UpperCamelCase   :						Union[str, Any]												=  after_output[0]
													_UpperCamelCase   :						int												=  np.amax(np.abs(out_a - out_a					)					)
													self.assertLessEqual(lowerCamelCase__					,1E-3					)





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							List[Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Any=None					,**lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						str												=  self.get_vision_text_model(lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  {'vision_model': vision_model, 'text_model': text_model}
							_UpperCamelCase   :						List[str]												=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__					)

							_UpperCamelCase   :						List[Any]												=  model(
							    input_ids=lowerCamelCase__					,pixel_values=lowerCamelCase__					,attention_mask=lowerCamelCase__					,output_attentions=lowerCamelCase__					)

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

							_UpperCamelCase   :						Any												=  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       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							pt_model.to(lowerCamelCase__					)
							pt_model.eval()

							# prepare inputs
							_UpperCamelCase   :						Optional[int]												=  inputs_dict
							_UpperCamelCase   :						List[str]												=  {k: torch.tensor(v.tolist()					) for k, v in flax_inputs.items()}

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

							_UpperCamelCase   :						Optional[Any]												=  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__					)
													_UpperCamelCase   :						Any												=  FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__					,from_pt=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  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__					)
													_UpperCamelCase   :						Tuple												=  VisionTextDualEncoderModel.from_pretrained(lowerCamelCase__					,from_flax=lowerCamelCase__					)

							pt_model_loaded.to(lowerCamelCase__					)
							pt_model_loaded.eval()

							with torch.no_grad():
													_UpperCamelCase   :						Tuple												=  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       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__					,lowerCamelCase__					)

							_UpperCamelCase   :						Dict												=  VisionTextDualEncoderModel(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  FlaxVisionTextDualEncoderModel(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  fx_state

							self.check_pt_flax_equivalence(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__					,lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, Any]												=  VisionTextDualEncoderModel(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  FlaxVisionTextDualEncoderModel(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  load_flax_weights_in_pytorch_model(lowerCamelCase__					,fx_model.params					)

							self.check_pt_flax_equivalence(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.prepare_config_and_inputs()
							self.check_save_load(**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**lowerCamelCase__					)





	@is_pt_flax_cross_test
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self.prepare_config_and_inputs()
							_UpperCamelCase   :						int												=  config_inputs_dict.pop('vision_config'					)
							_UpperCamelCase   :						List[str]												=  config_inputs_dict.pop('text_config'					)

							_UpperCamelCase   :						int												=  config_inputs_dict

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





	@slow
	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  self.get_pretrained_model_and_inputs()

							_UpperCamelCase   :						Dict												=  model_a(**lowerCamelCase__					)
							_UpperCamelCase   :						str												=  outputs[0]

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

													_UpperCamelCase   :						str												=  model_a(**lowerCamelCase__					)
													_UpperCamelCase   :						Optional[int]												=  after_outputs[0]
													_UpperCamelCase   :						Union[str, Any]												=  np.amax(np.abs(out_a - out_a					)					)
													self.assertLessEqual(lowerCamelCase__					,1E-5					)






@require_flax
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-vit'					,'hf-internal-testing/tiny-bert'					,vision_from_pt=lowerCamelCase__					,text_from_pt=lowerCamelCase__					,)
							_UpperCamelCase   :						Tuple												=  13
							_UpperCamelCase   :						Optional[int]												=  floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]					)
							_UpperCamelCase   :						str												=  ids_tensor([batch_size, 4]					,model.config.text_config.vocab_size					)
							_UpperCamelCase   :						Optional[Any]												=  random_attention_mask([batch_size, 4]					)
							_UpperCamelCase   :						Union[str, Any]												=  {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  FlaxViTModel(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  FlaxBertModel(lowerCamelCase__					)
							return vision_model, text_model





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  FlaxViTModelTester(self					)
							_UpperCamelCase   :						Tuple												=  FlaxBertModelTester(self					)
							_UpperCamelCase   :						Union[str, Any]												=  vit_model_tester.prepare_config_and_inputs()
							_UpperCamelCase   :						Dict												=  bert_model_tester.prepare_config_and_inputs()

							_UpperCamelCase							,     _UpperCamelCase   :						int												=  vision_config_and_inputs

							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  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__      (				lowercase		,   unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    'hf-internal-testing/tiny-random-clip'					,'hf-internal-testing/tiny-bert'					,vision_from_pt=lowerCamelCase__					,text_from_pt=lowerCamelCase__					,)
							_UpperCamelCase   :						str												=  13
							_UpperCamelCase   :						int												=  floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]					)
							_UpperCamelCase   :						Dict												=  ids_tensor([batch_size, 4]					,model.config.text_config.vocab_size					)
							_UpperCamelCase   :						Optional[int]												=  random_attention_mask([batch_size, 4]					)
							_UpperCamelCase   :						Union[str, Any]												=  {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

							return model, inputs





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							int					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  FlaxCLIPVisionModel(lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  FlaxBertModel(lowerCamelCase__					)
							return vision_model, text_model





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  FlaxCLIPVisionModelTester(self					)
							_UpperCamelCase   :						Tuple												=  FlaxBertModelTester(self					)
							_UpperCamelCase   :						Tuple												=  clip_model_tester.prepare_config_and_inputs()
							_UpperCamelCase   :						Optional[int]												=  bert_model_tester.prepare_config_and_inputs()

							_UpperCamelCase							,     _UpperCamelCase   :						Any												=  vision_config_and_inputs

							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						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  ):





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'					,logit_scale_init_value=1.0					)
							_UpperCamelCase   :						Optional[int]												=  VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'					)

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

							_UpperCamelCase   :						Optional[Any]												=  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])					,)

							_UpperCamelCase   :						Optional[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					,lowerCamelCase__					,atol=1E-3					)					)







'''simple docstring'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


