




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__				:      Dict      )     ->       List[Any]:





				"""simple docstring"""


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







@require_flax
class     __lowerCAmelCase :

			"""simple docstring"""







			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							pass


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.abs((a - b)							).max()
							self.assertLessEqual(UpperCamelCase__			,						UpperCamelCase__			,						f"""Difference between torch and flax is {diff} (>= {tol})."""							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      int:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[int]:



							'''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       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**UpperCamelCase__							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase__							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							self.check_save_load(**UpperCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**UpperCamelCase__							)


			@is_pt_flax_cross_test
			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__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     __lowerCAmelCase (       lowerCAmelCase__			,				unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      int:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    FlaxViTModel(UpperCamelCase__							)
							__lowerCamelCase		       =    FlaxBertModel(UpperCamelCase__							)
							return vision_model, text_model






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



							'''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		       =    vision_config_and_inputs

							__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     __lowerCAmelCase (       lowerCAmelCase__			,				unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      Tuple:



							'''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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    FlaxCLIPVisionModel(UpperCamelCase__							)
							__lowerCamelCase		       =    FlaxBertModel(UpperCamelCase__							)
							return vision_model, text_model






			def       lowercase_    (      self							)    ->      Dict:



							'''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		       =    vision_config_and_inputs

							__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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







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



							'''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							)							)








def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       lowerCamelCase   :      str			 =					(boundary[1] - boundary[0]) / steps
       lowerCamelCase   :      List[str]			 =					boundary[0]
       lowerCamelCase   :      Union[str, Any]			 =					boundary[1]
       lowerCamelCase   :      int			 =					make_points(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					0.0
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       for i in x_i:
              # print(i)
              y += h * f(_SCREAMING_SNAKE_CASE    )
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       return y

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      int			 =					a + h
       while x < (b - h):
              yield x
              lowerCamelCase   :      List[str]			 =					x + h

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:  # enter your function here
       lowerCamelCase   :      str			 =					(x - 0) * (x - 0)
       return y

def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					0.0  # Lower bound of integration
       lowerCamelCase   :      int			 =					1.0  # Upper bound of integration
       lowerCamelCase   :      Dict			 =					10.0  # define number of steps or resolution
       lowerCamelCase   :      int			 =					[a, b]  # define boundary of integration
       lowerCamelCase   :      str			 =					method_a(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''y = {y}'''    )


if __name__ == "__main__":
       main()






