






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				(      UpperCamelCase_    :   List[str]						)					->				Any:

						"""simple docstring"""



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


@require_flax
class       __SCREAMING_SNAKE_CASE   :






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel(_UpperCamelCase				)

												lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.get_vision_text_model(_UpperCamelCase       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  {'vision_model': vision_model, 'text_model': text_model}
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase				)

												lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.get_vision_text_model(_UpperCamelCase       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  {'vision_model': vision_model, 'text_model': text_model}
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase				)

												lowerCAmelCase__        =  model(input_ids=_UpperCamelCase       ,  pixel_values=_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)
												lowerCAmelCase__        =  output[0]

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

																		lowerCAmelCase__        =  model(input_ids=_UpperCamelCase       ,  pixel_values=_UpperCamelCase       ,  attention_mask=_UpperCamelCase				)
																		lowerCAmelCase__        =  after_output[0]
																		lowerCAmelCase__        =  np.amax(np.abs(out_a - out_a				)				)
																		self.assertLessEqual(_UpperCamelCase       ,  1E-3				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.get_vision_text_model(_UpperCamelCase       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  {'vision_model': vision_model, 'text_model': text_model}
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_UpperCamelCase				)

												lowerCAmelCase__        =  model(
												    input_ids=_UpperCamelCase       ,  pixel_values=_UpperCamelCase       ,  attention_mask=_UpperCamelCase       ,  output_attentions=_UpperCamelCase				)

												lowerCAmelCase__        =  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)
												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(_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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												pt_model.to(_UpperCamelCase				)
												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(**_UpperCamelCase				).to_tuple()

												lowerCAmelCase__        =  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				)
																		lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCamelCase       ,  from_pt=_UpperCamelCase				)

												lowerCAmelCase__        =  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				)
																		lowerCAmelCase__        =  VisionTextDualEncoderModel.from_pretrained(_UpperCamelCase       ,  from_flax=_UpperCamelCase				)

												pt_model_loaded.to(_UpperCamelCase				)
												pt_model_loaded.eval()

												with torch.no_grad():
																		lowerCAmelCase__        =  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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  VisionTextDualEncoderModel(_UpperCamelCase				)
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel(_UpperCamelCase				)

												lowerCAmelCase__        =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  fx_state

												self.check_pt_flax_equivalence(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  VisionTextDualEncoderConfig.from_vision_text_configs(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  VisionTextDualEncoderModel(_UpperCamelCase				)
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel(_UpperCamelCase				)

												lowerCAmelCase__        =  load_flax_weights_in_pytorch_model(_UpperCamelCase       ,  fx_model.params				)

												self.check_pt_flax_equivalence(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												self.check_model_from_pretrained_configs(**_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_from_pretrained(**_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												self.check_save_load(**_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												self.check_vision_text_output_attention(**_UpperCamelCase				)






						@is_pt_flax_cross_test
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)
												self.check_equivalence_flax_to_pt(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.get_pretrained_model_and_inputs()

												lowerCAmelCase__        =  model_a(**_UpperCamelCase				)
												lowerCAmelCase__        =  outputs[0]

												with tempfile.TemporaryDirectory() as tmp_dirname:
																		model_a.save_pretrained(_UpperCamelCase				)
																		lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_pretrained(_UpperCamelCase				)

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




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
												    'hf-internal-testing/tiny-random-vit'       ,  'hf-internal-testing/tiny-bert'       ,  vision_from_pt=_UpperCamelCase       ,  text_from_pt=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  13
												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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxViTModel(_UpperCamelCase				)
												lowerCAmelCase__        =  FlaxBertModel(_UpperCamelCase				)
												return vision_model, text_model







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
												    'hf-internal-testing/tiny-random-clip'       ,  'hf-internal-testing/tiny-bert'       ,  vision_from_pt=_UpperCamelCase       ,  text_from_pt=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  13
												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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxCLIPVisionModel(_UpperCamelCase				)
												lowerCAmelCase__        =  FlaxBertModel(_UpperCamelCase				)
												return vision_model, text_model







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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=_UpperCamelCase       ,  padding=_UpperCamelCase       ,  return_tensors='np'				)

												lowerCAmelCase__        =  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])       ,  )

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

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













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





