






'''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 	__UpperCAmelCase       ( __magic_name__						)->    Any:



			"""simple docstring"""



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







@require_flax
class    A_	:
							"""simple docstring"""




							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[int]				)				->  Optional[Any]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:List[str]				)				->  Optional[int]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Dict				)				->  int:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       np.abs((a - b)				).max()
										self.assertLessEqual(A_       ,							A_       ,							F'''Difference between torch and flax is {diff} (>= {tol}).'''				)

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Any=None       ,							**lowerCAmelCase__			:Union[str, Any]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       VisionTextDualEncoderConfig.from_vision_text_configs(A_       ,							A_				)

										snake_case_				:					Optional[int]   =       FlaxVisionTextDualEncoderModel(A_				)

										snake_case_				:					int   =       model(input_ids=A_       ,							pixel_values=A_       ,							attention_mask=A_				)

										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 							_A	(		self			:Dict       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[Any]=None       ,							**lowerCAmelCase__			:List[Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Optional[Any]   =       self.get_vision_text_model(A_       ,							A_				)
										snake_case_				:					List[str]   =       {"vision_model": vision_model, "text_model": text_model}
										snake_case_				:					Union[str, Any]   =       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_				)

										snake_case_				:					List[Any]   =       model(input_ids=A_       ,							pixel_values=A_       ,							attention_mask=A_				)

										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 							_A	(		self			:int       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Dict=None       ,							**lowerCAmelCase__			:Union[str, Any]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					List[Any]   =       self.get_vision_text_model(A_       ,							A_				)
										snake_case_				:					Union[str, Any]   =       {"vision_model": vision_model, "text_model": text_model}
										snake_case_				:					Dict   =       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_				)

										snake_case_				:					Any   =       model(input_ids=A_       ,							pixel_values=A_       ,							attention_mask=A_				)
										snake_case_				:					int   =       output[0]

										with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(A_				)
													snake_case_				:					Dict   =       FlaxVisionTextDualEncoderModel.from_pretrained(A_				)

													snake_case_				:					Dict   =       model(input_ids=A_       ,							pixel_values=A_       ,							attention_mask=A_				)
													snake_case_				:					Optional[Any]   =       after_output[0]
													snake_case_				:					List[Any]   =       np.amax(np.abs(out_a - out_a				)				)
													self.assertLessEqual(A_       ,							1E-3				)

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:int=None       ,							**lowerCAmelCase__			:List[str]				)				->  Dict:


										'''simple docstring'''




										snake_case_,	snake_case_				:					List[str]   =       self.get_vision_text_model(A_       ,							A_				)
										snake_case_				:					List[str]   =       {"vision_model": vision_model, "text_model": text_model}
										snake_case_				:					Optional[int]   =       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_				)

										snake_case_				:					Union[str, Any]   =       model(
										    input_ids=A_       ,							pixel_values=A_       ,							attention_mask=A_       ,							output_attentions=A_				)

										snake_case_				:					List[str]   =       output.vision_model_output.attentions
										self.assertEqual(len(A_				)       ,							vision_config.num_hidden_layers				)

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

										snake_case_				:					Optional[int]   =       output.text_model_output.attentions
										self.assertEqual(len(A_				)       ,							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 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Optional[Any]				)				->  List[str]:


										'''simple docstring'''




										pt_model.to(A_				)
										pt_model.eval()

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

										with torch.no_grad():
													snake_case_				:					Tuple   =       pt_model(**A_				).to_tuple()

										snake_case_				:					List[str]   =       fx_model(**A_				).to_tuple()
										self.assertEqual(len(A_				)       ,							len(A_				)       ,							"Output lengths differ between Flax and PyTorch"				)
										for fx_output, pt_output in zip(fx_outputs[:4]       ,							pt_outputs[:4]				):
													self.assert_almost_equals(A_       ,							pt_output.numpy()       ,							4E-2				)

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

										snake_case_				:					str   =       fx_model_loaded(**A_				).to_tuple()
										self.assertEqual(len(A_				)       ,							len(A_				)       ,							"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(A_       ,							pt_output.numpy()       ,							4E-2				)

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

										pt_model_loaded.to(A_				)
										pt_model_loaded.eval()

										with torch.no_grad():
													snake_case_				:					List[str]   =       pt_model_loaded(**A_				).to_tuple()

										self.assertEqual(len(A_				)       ,							len(A_				)       ,							"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(A_       ,							pt_output_loaded.numpy()       ,							4E-2				)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:List[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       VisionTextDualEncoderConfig.from_vision_text_configs(A_       ,							A_				)

										snake_case_				:					int   =       VisionTextDualEncoderModel(A_				)
										snake_case_				:					Any   =       FlaxVisionTextDualEncoderModel(A_				)

										snake_case_				:					List[Any]   =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,							A_				)
										snake_case_				:					List[Any]   =       fx_state

										self.check_pt_flax_equivalence(A_       ,							A_       ,							A_				)

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[int]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					int   =       VisionTextDualEncoderConfig.from_vision_text_configs(A_       ,							A_				)

										snake_case_				:					Tuple   =       VisionTextDualEncoderModel(A_				)
										snake_case_				:					Optional[Any]   =       FlaxVisionTextDualEncoderModel(A_				)

										snake_case_				:					int   =       load_flax_weights_in_pytorch_model(A_       ,							fx_model.params				)

										self.check_pt_flax_equivalence(A_       ,							A_       ,							A_				)

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.prepare_config_and_inputs()
										self.check_model_from_pretrained_configs(**A_				)

							def 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.prepare_config_and_inputs()
										self.check_vision_text_dual_encoder_from_pretrained(**A_				)

							def 							_A	(		self			:Tuple				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       self.prepare_config_and_inputs()
										self.check_save_load(**A_				)

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


										'''simple docstring'''




										snake_case_				:					Dict   =       self.prepare_config_and_inputs()
										self.check_vision_text_output_attention(**A_				)

							@is_pt_flax_cross_test
							def 							_A	(		self			:Tuple				)				->  str:


										'''simple docstring'''




										snake_case_				:					Any   =       self.prepare_config_and_inputs()
										snake_case_				:					Tuple   =       config_inputs_dict.pop("vision_config"				)
										snake_case_				:					Tuple   =       config_inputs_dict.pop("text_config"				)

										snake_case_				:					List[Any]   =       config_inputs_dict

										self.check_equivalence_pt_to_flax(A_       ,							A_       ,							A_				)
										self.check_equivalence_flax_to_pt(A_       ,							A_       ,							A_				)






							@slow
							def 							_A	(		self			:List[str]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Tuple   =       self.get_pretrained_model_and_inputs()

										snake_case_				:					Dict   =       model_a(**A_				)
										snake_case_				:					int   =       outputs[0]

										with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(A_				)
													snake_case_				:					Any   =       FlaxVisionTextDualEncoderModel.from_pretrained(A_				)

													snake_case_				:					Dict   =       model_a(**A_				)
													snake_case_				:					List[str]   =       after_outputs[0]
													snake_case_				:					Any   =       np.amax(np.abs(out_a - out_a				)				)
													self.assertLessEqual(A_       ,							1E-5				)







@require_flax
class    A_	(_SCREAMING_SNAKE_CASE						,					unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Union[str, Any]				)				->  Optional[Any]:


										'''simple docstring'''




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

										return model, inputs

							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					int   =       FlaxViTModel(A_				)
										snake_case_				:					Any   =       FlaxBertModel(A_				)
										return vision_model, text_model






							def 							_A	(		self			:Tuple				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       FlaxViTModelTester(self				)
										snake_case_				:					Any   =       FlaxBertModelTester(self				)
										snake_case_				:					Dict   =       vit_model_tester.prepare_config_and_inputs()
										snake_case_				:					Any   =       bert_model_tester.prepare_config_and_inputs()

										snake_case_,	snake_case_				:					Any   =       vision_config_and_inputs

										snake_case_,	snake_case_,	snake_case_,	snake_case_				:					Optional[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    A_	(_SCREAMING_SNAKE_CASE						,					unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[str]   =       FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
										    "hf-internal-testing/tiny-random-clip"       ,							"hf-internal-testing/tiny-bert"       ,							vision_from_pt=A_       ,							text_from_pt=A_       ,							)
										snake_case_				:					List[Any]   =       13
										snake_case_				:					int   =       floats_tensor(
										    [
										        batch_size,
										        model.config.vision_config.num_channels,
										        model.config.vision_config.image_size,
										        model.config.vision_config.image_size,
										    ]				)
										snake_case_				:					Tuple   =       ids_tensor([batch_size, 4]       ,							model.config.text_config.vocab_size				)
										snake_case_				:					str   =       random_attention_mask([batch_size, 4]				)
										snake_case_				:					List[Any]   =       {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

										return model, inputs

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					Dict   =       FlaxCLIPVisionModel(A_				)
										snake_case_				:					List[Any]   =       FlaxBertModel(A_				)
										return vision_model, text_model






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


										'''simple docstring'''




										snake_case_				:					str   =       FlaxCLIPVisionModelTester(self				)
										snake_case_				:					List[str]   =       FlaxBertModelTester(self				)
										snake_case_				:					int   =       clip_model_tester.prepare_config_and_inputs()
										snake_case_				:					List[Any]   =       bert_model_tester.prepare_config_and_inputs()

										snake_case_,	snake_case_				:					int   =       vision_config_and_inputs

										snake_case_,	snake_case_,	snake_case_,	snake_case_				:					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    A_	(unittest.TestCase							):
							"""simple docstring"""




							@slow
							def 							_A	(		self			:Union[str, Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"       ,							logit_scale_init_value=1.0				)
										snake_case_				:					Optional[Any]   =       VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"				)

										snake_case_				:					str   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
										snake_case_				:					Tuple   =       processor(
										    text=["una foto di un gatto", "una foto di un cane"]       ,							images=A_       ,							padding=A_       ,							return_tensors="np"				)

										snake_case_				:					Tuple   =       model(**A_				)

										# 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])       ,							)

										snake_case_				:					Tuple   =       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       ,							A_       ,							atol=1E-3				)				)












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




