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




@require_flax
class 					_SCREAMING_SNAKE_CASE  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          pass
      def A_			(			self ):
          pass
      def A_			(			self ):
          pass
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				np.abs((a - b) ).max()
          self.assertLessEqual(lowerCamelCase		,	lowerCamelCase		,	F"""Difference between torch and flax is {diff} (>= {tol}).""" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )

          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		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )

          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		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )
          snake_case__						=				output[0]

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(lowerCamelCase )
              snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase )

              snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )
              snake_case__						=				after_output[0]
              snake_case__						=				np.amax(np.abs(out_a - out_a ) )
              self.assertLessEqual(lowerCamelCase		,	1e-3 )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(
              input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase		,	output_attentions=lowerCamelCase )

          snake_case__						=				output.vision_model_output.attentions
          self.assertEqual(len(lowerCamelCase )		,	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__						=				to_atuple(vision_model.config.image_size )
          snake_case__						=				to_atuple(vision_model.config.patch_size )
          snake_case__						=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          snake_case__						=				num_patches + 1
          self.assertEqual(vision_attentions[0].shape[-3:]		,	(vision_config.num_attention_heads, seq_len, seq_len) )

          snake_case__						=				output.text_model_output.attentions
          self.assertEqual(len(lowerCamelCase )		,	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		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          pt_model.to(lowerCamelCase )
          pt_model.eval()

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

          with torch.no_grad():
              snake_case__						=				pt_model(**lowerCamelCase ).to_tuple()

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

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

          snake_case__						=				fx_model_loaded(**lowerCamelCase ).to_tuple()
          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase )		,	"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(lowerCamelCase		,	pt_output.numpy()		,	4e-2 )

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

          pt_model_loaded.to(lowerCamelCase )
          pt_model_loaded.eval()

          with torch.no_grad():
              snake_case__						=				pt_model_loaded(**lowerCamelCase ).to_tuple()

          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase )		,	"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(lowerCamelCase		,	pt_output_loaded.numpy()		,	4e-2 )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				VisionTextDualEncoderModel(lowerCamelCase )
          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,	lowerCamelCase )
          snake_case__						=				fx_state

          self.check_pt_flax_equivalence(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				VisionTextDualEncoderModel(lowerCamelCase )
          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				load_flax_weights_in_pytorch_model(lowerCamelCase		,	fx_model.params )

          self.check_pt_flax_equivalence(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_save_load(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**lowerCamelCase )
      @is_pt_flax_cross_test
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						=				config_inputs_dict.pop("vision_config" )
          snake_case__						=				config_inputs_dict.pop("text_config" )

          snake_case__						=				config_inputs_dict

          self.check_equivalence_pt_to_flax(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
          self.check_equivalence_flax_to_pt(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )



      @slow
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.get_pretrained_model_and_inputs()

          snake_case__						=				model_a(**lowerCamelCase )
          snake_case__						=				outputs[0]

          with tempfile.TemporaryDirectory() as tmp_dirname:
              model_a.save_pretrained(lowerCamelCase )
              snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase )

              snake_case__						=				model_a(**lowerCamelCase )
              snake_case__						=				after_outputs[0]
              snake_case__						=				np.amax(np.abs(out_a - out_a ) )
              self.assertLessEqual(lowerCamelCase		,	1e-5 )





@require_flax
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-vit"		,	"hf-internal-testing/tiny-bert"		,	vision_from_pt=lowerCamelCase		,	text_from_pt=lowerCamelCase		,	)
          snake_case__						=				13
          snake_case__						=				floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ] )
          snake_case__						=				ids_tensor([batch_size, 4]		,	model.config.text_config.vocab_size )
          snake_case__						=				random_attention_mask([batch_size, 4] )
          snake_case__						=				{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FlaxViTModel(lowerCamelCase )
          snake_case__						=				FlaxBertModel(lowerCamelCase )
          return vision_model, text_model



      def A_			(			self ):
          snake_case__						=				FlaxViTModelTester(self )
          snake_case__						=				FlaxBertModelTester(self )
          snake_case__						=				vit_model_tester.prepare_config_and_inputs()
          snake_case__						=				bert_model_tester.prepare_config_and_inputs()

          snake_case__						,					snake_case__						=				vision_config_and_inputs

          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				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  (      __UpperCamelCase       ,					unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-clip"		,	"hf-internal-testing/tiny-bert"		,	vision_from_pt=lowerCamelCase		,	text_from_pt=lowerCamelCase		,	)
          snake_case__						=				13
          snake_case__						=				floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ] )
          snake_case__						=				ids_tensor([batch_size, 4]		,	model.config.text_config.vocab_size )
          snake_case__						=				random_attention_mask([batch_size, 4] )
          snake_case__						=				{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FlaxCLIPVisionModel(lowerCamelCase )
          snake_case__						=				FlaxBertModel(lowerCamelCase )
          return vision_model, text_model



      def A_			(			self ):
          snake_case__						=				FlaxCLIPVisionModelTester(self )
          snake_case__						=				FlaxBertModelTester(self )
          snake_case__						=				clip_model_tester.prepare_config_and_inputs()
          snake_case__						=				bert_model_tester.prepare_config_and_inputs()

          snake_case__						,					snake_case__						=				vision_config_and_inputs

          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				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 A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"		,	logit_scale_init_value=1.0 )
          snake_case__						=				VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )

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

          snake_case__						=				model(**lowerCamelCase )

          # 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__						=				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		,	lowerCamelCase		,	atol=1e-3 ) )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) == 0:
        return False
    snake_case__						=				len(__lowerCAmelCase							) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]    ,		__lowerCAmelCase							)
    else:
        return binary_search(a_list[midpoint + 1 :]    ,		__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  input('''Enter numbers separated by comma:\n''').strip()
     __magic_name__            =  [int(item.strip()) for item in user_input.split(''',''')]
     __magic_name__            =  int(input('''Enter the number to be found in the list:\n''').strip())
     __magic_name__            =  '''''' if binary_search(sequence, target) else '''not '''
     print(F'''{target} was {not_str}found in {sequence}''')



