

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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





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


@require_flax
class 				SCREAMING_SNAKE_CASE__ :





    def 				snake_case_    (				self						,       a						,       a):
         pass





    def 				snake_case_    (				self):
         pass





    def 				snake_case_    (				self):
         pass





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Tuple				=	np.abs((a - b)).max()
         self.assertLessEqual(a						,       a						,       f"""Difference between torch and flax is {diff} (>= {tol}).""")





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a=None						,       **a):
         lowercase__   :       Union[str, Any]				=	VisionTextDualEncoderConfig.from_vision_text_configs(a						,       a)

         lowercase__   :       Union[str, Any]				=	FlaxVisionTextDualEncoderModel(a)

         lowercase__   :       Tuple				=	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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a=None						,       **a):
         lowercase__				,			lowercase__   :       Dict				=	self.get_vision_text_model(a						,       a)
         lowercase__   :       Dict				=	{'vision_model': vision_model, 'text_model': text_model}
         lowercase__   :       Tuple				=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a)

         lowercase__   :       int				=	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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a=None						,       **a):
         lowercase__				,			lowercase__   :       Union[str, Any]				=	self.get_vision_text_model(a						,       a)
         lowercase__   :       Dict				=	{'vision_model': vision_model, 'text_model': text_model}
         lowercase__   :       int				=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a)

         lowercase__   :       List[Any]				=	model(input_ids=a						,       pixel_values=a						,       attention_mask=a)
         lowercase__   :       Optional[int]				=	output[0]

         with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(a)
              lowercase__   :       Dict				=	FlaxVisionTextDualEncoderModel.from_pretrained(a)

              lowercase__   :       Dict				=	model(input_ids=a						,       pixel_values=a						,       attention_mask=a)
              lowercase__   :       int				=	after_output[0]
              lowercase__   :       Any				=	np.amax(np.abs(out_a - out_a))
              self.assertLessEqual(a						,       1e-3)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a=None						,       **a):
         lowercase__				,			lowercase__   :       Dict				=	self.get_vision_text_model(a						,       a)
         lowercase__   :       Dict				=	{'vision_model': vision_model, 'text_model': text_model}
         lowercase__   :       Optional[Any]				=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a)

         lowercase__   :       List[str]				=	model(
             input_ids=a						,       pixel_values=a						,       attention_mask=a						,       output_attentions=a)

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

         lowercase__   :       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 				snake_case_    (				self						,       a						,       a						,       a):
         pt_model.to(a)
         pt_model.eval()

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

         with torch.no_grad():
              lowercase__   :       int				=	pt_model(**a).to_tuple()

         lowercase__   :       Tuple				=	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)
              lowercase__   :       Union[str, Any]				=	FlaxVisionTextDualEncoderModel.from_pretrained(a						,       from_pt=a)

         lowercase__   :       Optional[Any]				=	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)
              lowercase__   :       Optional[int]				=	VisionTextDualEncoderModel.from_pretrained(a						,       from_flax=a)

         pt_model_loaded.to(a)
         pt_model_loaded.eval()

         with torch.no_grad():
              lowercase__   :       int				=	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 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       str				=	VisionTextDualEncoderConfig.from_vision_text_configs(a						,       a)

         lowercase__   :       List[str]				=	VisionTextDualEncoderModel(a)
         lowercase__   :       Union[str, Any]				=	FlaxVisionTextDualEncoderModel(a)

         lowercase__   :       int				=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,       a)
         lowercase__   :       Any				=	fx_state

         self.check_pt_flax_equivalence(a						,       a						,       a)





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Optional[Any]				=	VisionTextDualEncoderConfig.from_vision_text_configs(a						,       a)

         lowercase__   :       Union[str, Any]				=	VisionTextDualEncoderModel(a)
         lowercase__   :       Any				=	FlaxVisionTextDualEncoderModel(a)

         lowercase__   :       int				=	load_flax_weights_in_pytorch_model(a						,       fx_model.params)

         self.check_pt_flax_equivalence(a						,       a						,       a)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**a)





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**a)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	self.prepare_config_and_inputs()
         self.check_save_load(**a)





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**a)





    @is_pt_flax_cross_test
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.prepare_config_and_inputs()
         lowercase__   :       int				=	config_inputs_dict.pop('vision_config')
         lowercase__   :       Any				=	config_inputs_dict.pop('text_config')

         lowercase__   :       int				=	config_inputs_dict

         self.check_equivalence_pt_to_flax(a						,       a						,       a)
         self.check_equivalence_flax_to_pt(a						,       a						,       a)





    @slow
    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       Dict				=	self.get_pretrained_model_and_inputs()

         lowercase__   :       Dict				=	model_a(**a)
         lowercase__   :       List[Any]				=	outputs[0]

         with tempfile.TemporaryDirectory() as tmp_dirname:
              model_a.save_pretrained(a)
              lowercase__   :       Any				=	FlaxVisionTextDualEncoderModel.from_pretrained(a)

              lowercase__   :       str				=	model_a(**a)
              lowercase__   :       Optional[Any]				=	after_outputs[0]
              lowercase__   :       Tuple				=	np.amax(np.abs(out_a - out_a))
              self.assertLessEqual(a						,       1e-5)



@require_flax
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):





    def 				snake_case_    (				self):
         lowercase__   :       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						,       )
         lowercase__   :       Union[str, Any]				=	13
         lowercase__   :       List[Any]				=	floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ])
         lowercase__   :       Optional[Any]				=	ids_tensor([batch_size, 4]						,       model.config.text_config.vocab_size)
         lowercase__   :       int				=	random_attention_mask([batch_size, 4])
         lowercase__   :       Union[str, Any]				=	{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

         return model, inputs





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       Union[str, Any]				=	FlaxViTModel(a)
         lowercase__   :       List[str]				=	FlaxBertModel(a)
         return vision_model, text_model





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

         lowercase__				,			lowercase__   :       Union[str, Any]				=	vision_config_and_inputs

         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       Union[str, 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 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):





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

         return model, inputs





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       str				=	FlaxCLIPVisionModel(a)
         lowercase__   :       Optional[int]				=	FlaxBertModel(a)
         return vision_model, text_model





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	FlaxCLIPVisionModelTester(self)
         lowercase__   :       Any				=	FlaxBertModelTester(self)
         lowercase__   :       Union[str, Any]				=	clip_model_tester.prepare_config_and_inputs()
         lowercase__   :       Optional[int]				=	bert_model_tester.prepare_config_and_inputs()

         lowercase__				,			lowercase__   :       Dict				=	vision_config_and_inputs

         lowercase__				,			lowercase__				,			lowercase__				,			lowercase__   :       str				=	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 				snake_case_    (				self):
         lowercase__   :       int				=	FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'						,       logit_scale_init_value=1.0)
         lowercase__   :       Optional[int]				=	VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian')

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

         lowercase__   :       List[Any]				=	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])						,       )

         lowercase__   :       Any				=	np.array([[1.2_284_727, 0.3_104_122]])

         self.assertTrue(np.allclose(outputs.logits_per_image						,       a						,       atol=1e-3))




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

