



"""simple docstring"""
from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
      from transformers import (
          TFBertModel,
          TFCLIPVisionModel,
          TFDeiTModel,
          TFRobertaModel,
          TFVisionTextDualEncoderModel,
          TFViTModel,
          VisionTextDualEncoderConfig,
      )

if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor

def       _snake_case						(						UpperCamelCase						:		List[str]						):
 if isinstance(UpperCamelCase			,		collections.abc.Iterable						):
  return x
 return (x, x)



@require_tf
class       SCREAMING_SNAKE_CASE__  :

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    pass

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





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					int	=	TFVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	model(input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)

    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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE      ,     text_model=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	model(input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)

    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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					int	=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	{"""vision_model""": vision_model, """text_model""": text_model}
    UpperCAmelCase	:					List[Any]	=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	model(input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)

    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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE      ,     text_model=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	model(input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	output[0].numpy()

    with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					List[Any]	=	TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					Optional[int]	=	model(input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Any	=	after_output[0].numpy()
     UpperCAmelCase	:					int	=	np.amax(np.abs(out_a - out_a						)						)
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-5						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE      ,     text_model=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	model(
        input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE      ,     output_attentions=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Any	=	output.vision_model_output.attentions
    self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     vision_config.num_hidden_layers						)

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

    UpperCAmelCase	:					List[Any]	=	output.text_model_output.attentions
    self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	np.abs((a - b)						).max()
    self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     F"Difference between torch and flax is {diff} (>= {tol})."						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_model(**_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.prepare_config_and_inputs()
    self.check_save_load(**_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE						)




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	self.get_pretrained_model_and_inputs()

    UpperCAmelCase	:					List[Any]	=	model_a(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	outputs[0].numpy()

    with tempfile.TemporaryDirectory() as tmp_dirname:
     model_a.save_pretrained(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Tuple	=	TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

     UpperCAmelCase	:					List[str]	=	model_a(**_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	after_outputs[0].numpy()
     UpperCAmelCase	:					List[Any]	=	np.amax(np.abs(out_a - out_a						)						)
     self.assertLessEqual(_SCREAMING_SNAKE_CASE      ,     1E-5						)



@require_tf
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """hf-internal-testing/tiny-random-vit"""      ,     """hf-internal-testing/tiny-random-bert"""						)
    UpperCAmelCase	:					int	=	13
    UpperCAmelCase	:					Optional[int]	=	floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ]						)
    UpperCAmelCase	:					Any	=	ids_tensor([batch_size, 4]      ,     model.text_model.config.vocab_size						)
    UpperCAmelCase	:					int	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	TFViTModel(_SCREAMING_SNAKE_CASE      ,     name="""vision_model"""						)
    UpperCAmelCase	:					Optional[Any]	=	TFBertModel(_SCREAMING_SNAKE_CASE      ,     name="""text_model"""						)
    return vision_model, text_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	TFViTModelTester(self						)
    UpperCAmelCase	:					int	=	TFBertModelTester(self						)
    UpperCAmelCase	:					str	=	vit_model_tester.prepare_config_and_inputs()
    UpperCAmelCase	:					int	=	bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					str	=	vision_config_and_inputs

    (
        (
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		
    )	:					str	=	text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }



@require_tf
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """Rocketknight1/tiny-random-deit-tf"""      ,     """hf-internal-testing/tiny-random-roberta"""						)
    UpperCAmelCase	:					Union[str, Any]	=	13
    UpperCAmelCase	:					Optional[Any]	=	floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ]						)
    UpperCAmelCase	:					List[Any]	=	ids_tensor([batch_size, 4]      ,     model.text_model.config.vocab_size						)
    UpperCAmelCase	:					Optional[Any]	=	random_attention_mask([batch_size, 4]						)
    UpperCAmelCase	:					Tuple	=	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					str	=	self.get_vision_text_model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE      ,     text_model=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	model(
        input_ids=_SCREAMING_SNAKE_CASE      ,     pixel_values=_SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE      ,     output_attentions=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	output.vision_model_output.attentions
    self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     vision_config.num_hidden_layers						)

    # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
    UpperCAmelCase	:					Dict	=	to_atuple(vision_model.config.image_size						)
    UpperCAmelCase	:					Tuple	=	to_atuple(vision_model.config.patch_size						)
    UpperCAmelCase	:					Union[str, Any]	=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    UpperCAmelCase	:					List[str]	=	num_patches + 2
    self.assertEqual(vision_attentions[0].shape[-3:]      ,     (vision_config.num_attention_heads, seq_len, seq_len)						)

    UpperCAmelCase	:					List[str]	=	output.text_model_output.attentions
    self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	TFDeiTModel(_SCREAMING_SNAKE_CASE      ,     name="""vision_model"""						)
    UpperCAmelCase	:					Union[str, Any]	=	TFRobertaModel(_SCREAMING_SNAKE_CASE      ,     name="""text_model"""						)
    return vision_model, text_model




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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	TFDeiTModelTester(self						)
    UpperCAmelCase	:					List[Any]	=	TFRobertaModelTester(self						)
    UpperCAmelCase	:					List[Any]	=	vit_model_tester.prepare_config_and_inputs()
    UpperCAmelCase	:					Optional[int]	=	bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Tuple	=	vision_config_and_inputs

    (
        (
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		
    )	:					Any	=	text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }



@require_tf
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	TFVisionTextDualEncoderModel.from_vision_text_pretrained(
        """Rocketknight1/tiny-random-clip-tf"""      ,     """hf-internal-testing/tiny-random-bert"""						)
    UpperCAmelCase	:					Optional[int]	=	13
    UpperCAmelCase	:					Union[str, Any]	=	floats_tensor(
        [
            batch_size,
            model.vision_model.config.num_channels,
            model.vision_model.config.image_size,
            model.vision_model.config.image_size,
        ]						)
    UpperCAmelCase	:					Optional[Any]	=	ids_tensor([batch_size, 4]      ,     model.text_model.config.vocab_size						)
    UpperCAmelCase	:					Union[str, Any]	=	random_attention_mask([batch_size, 4]						)
    UpperCAmelCase	:					Optional[int]	=	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

    return model, inputs

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	TFCLIPVisionModel(_SCREAMING_SNAKE_CASE      ,     name="""vision_model"""						)
    UpperCAmelCase	:					List[str]	=	TFBertModel(_SCREAMING_SNAKE_CASE      ,     name="""text_model"""						)
    return vision_model, text_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	TFCLIPVisionModelTester(self						)
    UpperCAmelCase	:					Dict	=	TFBertModelTester(self						)
    UpperCAmelCase	:					str	=	clip_model_tester.prepare_config_and_inputs()
    UpperCAmelCase	:					List[str]	=	bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	vision_config_and_inputs

    (
        (
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		
    )	:					Dict	=	text_config_and_inputs

    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": input_mask,
        "input_ids": input_ids,
        "text_token_type_ids": token_type_ids,
        "text_sequence_labels": sequence_labels,
        "text_token_labels": token_labels,
        "text_choice_labels": choice_labels,
    }



@require_vision
@require_tf
class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	TFVisionTextDualEncoderModel.from_pretrained(
        """clip-italian/clip-italian"""      ,     logit_scale_init_value=1.0      ,     from_pt=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""						)

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

    UpperCAmelCase	:					Optional[int]	=	model(**_SCREAMING_SNAKE_CASE						)

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

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





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





