





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_					(						__UpperCAmelCase:    Tuple   )   ->			str:
				if isinstance(__UpperCAmelCase	,     collections.abc.Iterable   ):
								return x
				return (x, x)






@require_flax
class 					lowercase__ :

			'''simple docstring'''


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






							"""simple docstring"""
							pass




			def    UpperCamelCase__						(					self							)				->   Any:






							"""simple docstring"""
							pass




			def    UpperCamelCase__						(					self							)				->   List[Any]:






							"""simple docstring"""
							pass




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   List[Any]:






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




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=None,	**__magic_name__							)				->   Union[str, Any]:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   VisionTextDualEncoderConfig.from_vision_text_configs(__magic_name__,	__magic_name__							)

							UpperCamelCase__							:       List[Any]      =   FlaxVisionTextDualEncoderModel(__magic_name__							)

							UpperCamelCase__							:       Optional[Any]      =   model(input_ids=__magic_name__,	pixel_values=__magic_name__,	attention_mask=__magic_name__							)

							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,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=None,	**__magic_name__							)				->   int:






							"""simple docstring"""
							UpperCamelCase__     ,UpperCamelCase__							:       Union[str, Any]      =   self.get_vision_text_model(__magic_name__,	__magic_name__							)
							UpperCamelCase__							:       Optional[Any]      =   {'''vision_model''': vision_model, '''text_model''': text_model}
							UpperCamelCase__							:       Optional[int]      =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__magic_name__							)

							UpperCamelCase__							:       Optional[Any]      =   model(input_ids=__magic_name__,	pixel_values=__magic_name__,	attention_mask=__magic_name__							)

							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,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=None,	**__magic_name__							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   self.get_vision_text_model(__magic_name__,	__magic_name__							)
							UpperCamelCase__							:       str      =   {'''vision_model''': vision_model, '''text_model''': text_model}
							UpperCamelCase__							:       Optional[Any]      =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__magic_name__							)

							UpperCamelCase__							:       Union[str, Any]      =   model(input_ids=__magic_name__,	pixel_values=__magic_name__,	attention_mask=__magic_name__							)
							UpperCamelCase__							:       Dict      =   output[0]

							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(__magic_name__							)
											UpperCamelCase__							:       Optional[Any]      =   FlaxVisionTextDualEncoderModel.from_pretrained(__magic_name__							)

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




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=None,	**__magic_name__							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.get_vision_text_model(__magic_name__,	__magic_name__							)
							UpperCamelCase__							:       Any      =   {'''vision_model''': vision_model, '''text_model''': text_model}
							UpperCamelCase__							:       Union[str, Any]      =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__magic_name__							)

							UpperCamelCase__							:       Dict      =   model(
							    input_ids=__magic_name__,	pixel_values=__magic_name__,	attention_mask=__magic_name__,	output_attentions=__magic_name__							)

							UpperCamelCase__							:       Optional[Any]      =   output.vision_model_output.attentions
							self.assertEqual(len(__magic_name__							),	vision_config.num_hidden_layers							)

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

							UpperCamelCase__							:       str      =   output.text_model_output.attentions
							self.assertEqual(len(__magic_name__							),	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,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






							"""simple docstring"""
							pt_model.to(__magic_name__							)
							pt_model.eval()

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

							with torch.no_grad():
											UpperCamelCase__							:       Tuple      =   pt_model(**__magic_name__							).to_tuple()

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

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

							UpperCamelCase__							:       Any      =   fx_model_loaded(**__magic_name__							).to_tuple()
							self.assertEqual(len(__magic_name__							),	len(__magic_name__							),	'''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(__magic_name__,	pt_output.numpy(),	4E-2							)

							# Flax -> PT
							with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__magic_name__							)
											UpperCamelCase__							:       List[Any]      =   VisionTextDualEncoderModel.from_pretrained(__magic_name__,	from_flax=__magic_name__							)

							pt_model_loaded.to(__magic_name__							)
							pt_model_loaded.eval()

							with torch.no_grad():
											UpperCamelCase__							:       Tuple      =   pt_model_loaded(**__magic_name__							).to_tuple()

							self.assertEqual(len(__magic_name__							),	len(__magic_name__							),	'''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(__magic_name__,	pt_output_loaded.numpy(),	4E-2							)




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   VisionTextDualEncoderConfig.from_vision_text_configs(__magic_name__,	__magic_name__							)

							UpperCamelCase__							:       List[str]      =   VisionTextDualEncoderModel(__magic_name__							)
							UpperCamelCase__							:       List[Any]      =   FlaxVisionTextDualEncoderModel(__magic_name__							)

							UpperCamelCase__							:       str      =   convert_pytorch_state_dict_to_flax(pt_model.state_dict(),	__magic_name__							)
							UpperCamelCase__							:       Optional[int]      =   fx_state

							self.check_pt_flax_equivalence(__magic_name__,	__magic_name__,	__magic_name__							)




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   VisionTextDualEncoderConfig.from_vision_text_configs(__magic_name__,	__magic_name__							)

							UpperCamelCase__							:       Optional[int]      =   VisionTextDualEncoderModel(__magic_name__							)
							UpperCamelCase__							:       Optional[int]      =   FlaxVisionTextDualEncoderModel(__magic_name__							)

							UpperCamelCase__							:       List[str]      =   load_flax_weights_in_pytorch_model(__magic_name__,	fx_model.params							)

							self.check_pt_flax_equivalence(__magic_name__,	__magic_name__,	__magic_name__							)




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






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**__magic_name__							)




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






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**__magic_name__							)




			def    UpperCamelCase__						(					self							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   self.prepare_config_and_inputs()
							self.check_save_load(**__magic_name__							)




			def    UpperCamelCase__						(					self							)				->   Optional[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**__magic_name__							)




			@is_pt_flax_cross_test
			def    UpperCamelCase__						(					self							)				->   Optional[int]:






							"""simple docstring"""
							UpperCamelCase__							:       Tuple      =   self.prepare_config_and_inputs()
							UpperCamelCase__							:       List[str]      =   config_inputs_dict.pop('''vision_config'''							)
							UpperCamelCase__							:       Dict      =   config_inputs_dict.pop('''text_config'''							)

							UpperCamelCase__							:       List[str]      =   config_inputs_dict

							self.check_equivalence_pt_to_flax(__magic_name__,	__magic_name__,	__magic_name__							)
							self.check_equivalence_flax_to_pt(__magic_name__,	__magic_name__,	__magic_name__							)




			@slow
			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.get_pretrained_model_and_inputs()

							UpperCamelCase__							:       int      =   model_a(**__magic_name__							)
							UpperCamelCase__							:       Any      =   outputs[0]

							with tempfile.TemporaryDirectory() as tmp_dirname:
											model_a.save_pretrained(__magic_name__							)
											UpperCamelCase__							:       Optional[Any]      =   FlaxVisionTextDualEncoderModel.from_pretrained(__magic_name__							)

											UpperCamelCase__							:       Union[str, Any]      =   model_a(**__magic_name__							)
											UpperCamelCase__							:       Tuple      =   after_outputs[0]
											UpperCamelCase__							:       str      =   np.amax(np.abs(out_a - out_a							)							)
											self.assertLessEqual(__magic_name__,	1E-5							)






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

			'''simple docstring'''


			def    UpperCamelCase__						(					self							)				->   Optional[int]:






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

							return model, inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   FlaxViTModel(__magic_name__							)
							UpperCamelCase__							:       Any      =   FlaxBertModel(__magic_name__							)
							return vision_model, text_model




			def    UpperCamelCase__						(					self							)				->   List[str]:






							"""simple docstring"""
							UpperCamelCase__							:       str      =   FlaxViTModelTester(self							)
							UpperCamelCase__							:       Optional[int]      =   FlaxBertModelTester(self							)
							UpperCamelCase__							:       Dict      =   vit_model_tester.prepare_config_and_inputs()
							UpperCamelCase__							:       List[str]      =   bert_model_tester.prepare_config_and_inputs()

							UpperCamelCase__     ,UpperCamelCase__							:       int      =   vision_config_and_inputs

							UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[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    UpperCamelCase__						(					self							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Any      =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    '''hf-internal-testing/tiny-random-clip''',	'''hf-internal-testing/tiny-bert''',	vision_from_pt=__magic_name__,	text_from_pt=__magic_name__,	)
							UpperCamelCase__							:       Dict      =   13
							UpperCamelCase__							:       List[str]      =   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__							:       Dict      =   random_attention_mask([batch_size, 4]							)
							UpperCamelCase__							:       List[str]      =   {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

							return model, inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   FlaxCLIPVisionModel(__magic_name__							)
							UpperCamelCase__							:       str      =   FlaxBertModel(__magic_name__							)
							return vision_model, text_model




			def    UpperCamelCase__						(					self							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   FlaxCLIPVisionModelTester(self							)
							UpperCamelCase__							:       str      =   FlaxBertModelTester(self							)
							UpperCamelCase__							:       Dict      =   clip_model_tester.prepare_config_and_inputs()
							UpperCamelCase__							:       Dict      =   bert_model_tester.prepare_config_and_inputs()

							UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   vision_config_and_inputs

							UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   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    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian''',	logit_scale_init_value=1.0							)
							UpperCamelCase__							:       int      =   VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''							)

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

							UpperCamelCase__							:       str      =   model(**__magic_name__							)

							# 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[int]      =   np.array([[1.228_4727, 0.310_4122]]							)

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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

