"""simple docstring"""



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(	a				):



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






@require_flax
class  snake_case__      :


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        pass


 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        pass


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    np.abs((a - b)						).max()
        self.assertLessEqual(__a    ,		__a    ,		F"Difference between torch and flax is {diff} (>= {tol})."						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
        __a       =    VisionTextDualEncoderConfig.from_vision_text_configs(__a    ,		__a						)

        __a       =    FlaxVisionTextDualEncoderModel(__a						)

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

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

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

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

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


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

        __a       =    model(
            input_ids=__a    ,		pixel_values=__a    ,		attention_mask=__a    ,		output_attentions=__a						)

        __a       =    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)
        __a       =    to_atuple(vision_model.config.image_size						)
        __a       =    to_atuple(vision_model.config.patch_size						)
        __a       =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        __a       =    num_patches + 1
        self.assertEqual(vision_attentions[0].shape[-3:]    ,		(vision_config.num_attention_heads, seq_len, seq_len)						)

        __a       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        pt_model.to(__a						)
        pt_model.eval()

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

        with torch.no_grad():
               __a       =    pt_model(**__a						).to_tuple()

        __a       =    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						)
               __a       =    FlaxVisionTextDualEncoderModel.from_pretrained(__a    ,		from_pt=__a						)

        __a       =    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						)
               __a       =    VisionTextDualEncoderModel.from_pretrained(__a    ,		from_flax=__a						)

        pt_model_loaded.to(__a						)
        pt_model_loaded.eval()

        with torch.no_grad():
               __a       =    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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    VisionTextDualEncoderConfig.from_vision_text_configs(__a    ,		__a						)

        __a       =    VisionTextDualEncoderModel(__a						)
        __a       =    FlaxVisionTextDualEncoderModel(__a						)

        __a       =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,		__a						)
        __a       =    fx_state

        self.check_pt_flax_equivalence(__a    ,		__a    ,		__a						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    VisionTextDualEncoderConfig.from_vision_text_configs(__a    ,		__a						)

        __a       =    VisionTextDualEncoderModel(__a						)
        __a       =    FlaxVisionTextDualEncoderModel(__a						)

        __a       =    load_flax_weights_in_pytorch_model(__a    ,		fx_model.params						)

        self.check_pt_flax_equivalence(__a    ,		__a    ,		__a						)


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        self.check_model_from_pretrained_configs(**__a						)


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        self.check_vision_text_dual_encoder_from_pretrained(**__a						)


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        self.check_save_load(**__a						)


 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        self.check_vision_text_output_attention(**__a						)


 @is_pt_flax_cross_test
 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        __a       =    config_inputs_dict.pop("vision_config"						)
        __a       =    config_inputs_dict.pop("text_config"						)

        __a       =    config_inputs_dict

        self.check_equivalence_pt_to_flax(__a    ,		__a    ,		__a						)
        self.check_equivalence_flax_to_pt(__a    ,		__a    ,		__a						)






 @slow
 def 						a__   ( self						):
        __a ,   __a       =    self.get_pretrained_model_and_inputs()

        __a       =    model_a(**__a						)
        __a       =    outputs[0]

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

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






@require_flax
class  snake_case__      (       UpperCAmelCase_,						unittest.TestCase       ):


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

        return model, inputs


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    FlaxViTModel(__a						)
        __a       =    FlaxBertModel(__a						)
        return vision_model, text_model






 def 						a__   ( self						):
        __a       =    FlaxViTModelTester(self						)
        __a       =    FlaxBertModelTester(self						)
        __a       =    vit_model_tester.prepare_config_and_inputs()
        __a       =    bert_model_tester.prepare_config_and_inputs()

        __a ,   __a       =    vision_config_and_inputs

        __a ,   __a ,   __a ,   __a       =    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  snake_case__      (       UpperCAmelCase_,						unittest.TestCase       ):


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

        return model, inputs


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    FlaxCLIPVisionModel(__a						)
        __a       =    FlaxBertModel(__a						)
        return vision_model, text_model






 def 						a__   ( self						):
        __a       =    FlaxCLIPVisionModelTester(self						)
        __a       =    FlaxBertModelTester(self						)
        __a       =    clip_model_tester.prepare_config_and_inputs()
        __a       =    bert_model_tester.prepare_config_and_inputs()

        __a ,   __a       =    vision_config_and_inputs

        __a ,   __a ,   __a ,   __a       =    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  snake_case__      (       unittest.TestCase       ):


 @slow
 def 						a__   ( self						):
        __a       =    FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"    ,		logit_scale_init_value=1.0						)
        __a       =    VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"						)

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

        __a       =    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])    ,		)

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

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





import socket

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		socket.socket(socket.AF_INET , socket.SOCK_STREAM			)
      lowerCamelCase             =		socket.gethostname()
      lowerCamelCase             =		12312

      sock.connect((host, port)			)
      sock.send(B"Hello server!"			)

      with open("Received_file" , "wb"			) as out_file:
            print("File opened"			)
            print("Receiving data..."			)
            while True:
                  lowerCamelCase             =		sock.recv(1024			)
                  if not data:
                        break
                  out_file.write(UpperCAmelCase__			)

      print("Successfully received the file"			)
      sock.close()
      print("Connection closed"			)


if __name__ == "__main__":
     main()