





'''simple docstring'''


import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
							from transformers.models.rag.configuration_rag import RagConfig
							from transformers.models.rag.tokenization_rag import RagTokenizer

@require_faiss
@require_torch
class    lowerCAmelCase_		(		UpperCAmelCase_		):




	def 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      tempfile.mkdtemp()
					_lowerCAmelCase				=      8

					# DPR tok
					_lowerCAmelCase				=      [
					    "[UNK]",
					    "[CLS]",
					    "[SEP]",
					    "[PAD]",
					    "[MASK]",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					_lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "dpr_tokenizer"		)
					os.makedirs(_lowercase			,     exist_ok=_lowercase		)
					_lowerCAmelCase				=      os.path.join(_lowercase			,     DPR_VOCAB_FILES_NAMES["vocab_file"]		)
					with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

					# BART tok
					_lowerCAmelCase				=      [
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					_lowerCAmelCase				=      dict(zip(_lowercase			,     range(len(_lowercase		)		)		)		)
					_lowerCAmelCase				=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					_lowerCAmelCase				=      {"unk_token": "<unk>"}

					_lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "bart_tokenizer"		)
					os.makedirs(_lowercase			,     exist_ok=_lowercase		)
					_lowerCAmelCase				=      os.path.join(_lowercase			,     BART_VOCAB_FILES_NAMES["vocab_file"]		)
					_lowerCAmelCase				=      os.path.join(_lowercase			,     BART_VOCAB_FILES_NAMES["merges_file"]		)
					with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as fp:
									fp.write(json.dumps(_lowercase		) + "\n"		)
					with open(self.merges_file			,     "w"			,     encoding="utf-8"		) as fp:
									fp.write("\n".join(_lowercase		)		)




	def 							_snake_case   (	self		)    ->							List[str]:
					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "dpr_tokenizer"		)		)




	def 							_snake_case   (	self		)    ->							int:
					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "bart_tokenizer"		)		)




	def 							_snake_case   (	self		)    ->							Optional[int]:
					shutil.rmtree(self.tmpdirname		)




	@require_tokenizers
	def 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "rag_tokenizer"		)
					_lowerCAmelCase				=      RagConfig(question_encoder=DPRConfig().to_dict()			,     generator=BartConfig().to_dict()		)
					_lowerCAmelCase				=      RagTokenizer(question_encoder=self.get_dpr_tokenizer()			,     generator=self.get_bart_tokenizer()		)
					rag_config.save_pretrained(_lowercase		)
					rag_tokenizer.save_pretrained(_lowercase		)
					_lowerCAmelCase				=      RagTokenizer.from_pretrained(_lowercase			,     config=_lowercase		)
					self.assertIsInstance(new_rag_tokenizer.question_encoder			,     _lowercase		)
					self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()			,     rag_tokenizer.question_encoder.get_vocab()		)
					self.assertIsInstance(new_rag_tokenizer.generator			,     _lowercase		)
					self.assertEqual(new_rag_tokenizer.generator.get_vocab()			,     rag_tokenizer.generator.get_vocab()		)




	@slow
	def 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      RagTokenizer.from_pretrained("facebook/rag-token-nq"		)
					_lowerCAmelCase				=      [
					    "who got the first nobel prize in physics",
					    "when is the next deadpool movie being released",
					    "which mode is used for short wave broadcast service",
					    "who is the owner of reading football club",
					    "when is the next scandal episode coming out",
					    "when is the last time the philadelphia won the superbowl",
					    "what is the most current adobe flash player version",
					    "how many episodes are there in dragon ball z",
					    "what is the first step in the evolution of the eye",
					    "where is gall bladder situated in human body",
					    "what is the main mineral in lithium batteries",
					    "who is the president of usa right now",
					    "where do the greasers live in the outsiders",
					    "panda is a national animal of which country",
					    "what is the name of manchester united stadium",
					]
					_lowerCAmelCase				=      tokenizer(_lowercase		)
					self.assertIsNotNone(_lowercase		)





	@slow
	def 							_snake_case   (	self		)    ->							Tuple:
					_lowerCAmelCase				=      RagTokenizer.from_pretrained("facebook/rag-sequence-nq"		)
					_lowerCAmelCase				=      [
					    "who got the first nobel prize in physics",
					    "when is the next deadpool movie being released",
					    "which mode is used for short wave broadcast service",
					    "who is the owner of reading football club",
					    "when is the next scandal episode coming out",
					    "when is the last time the philadelphia won the superbowl",
					    "what is the most current adobe flash player version",
					    "how many episodes are there in dragon ball z",
					    "what is the first step in the evolution of the eye",
					    "where is gall bladder situated in human body",
					    "what is the main mineral in lithium batteries",
					    "who is the president of usa right now",
					    "where do the greasers live in the outsiders",
					    "panda is a national animal of which country",
					    "what is the name of manchester united stadium",
					]
					_lowerCAmelCase				=      tokenizer(_lowercase		)
					self.assertIsNotNone(_lowercase		)









'''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 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





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

@require_tf
class    lowerCAmelCase_		:




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     pass




 def 							_snake_case   (	self		)    ->							Dict:
     pass




 def 							_snake_case   (	self		)    ->							int:
     pass




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(_lowerCAmelCase		)

     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      {"vision_model": vision_model, "text_model": text_model}
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase		)

     _lowerCAmelCase				=      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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

     _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)
     _lowerCAmelCase				=      output[0].numpy()

     with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase		)

         _lowerCAmelCase				=      model(input_ids=_lowerCAmelCase			,     pixel_values=_lowerCAmelCase			,     attention_mask=_lowerCAmelCase		)
         _lowerCAmelCase				=      after_output[0].numpy()
         _lowerCAmelCase				=      np.amax(np.abs(out_a - out_a		)		)
         self.assertLessEqual(_lowerCAmelCase			,     1E-5		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      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)
     _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(_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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase				=      np.abs((a - b)		).max()
     self.assertLessEqual(_lowerCAmelCase			,     _lowerCAmelCase			,     f'''Difference between torch and flax is {diff} (>= {tol}).'''		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_model(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_save_load(**_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**_lowerCAmelCase		)





 @slow
 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_pretrained_model_and_inputs()

     _lowerCAmelCase				=      model_a(**_lowerCAmelCase		)
     _lowerCAmelCase				=      outputs[0].numpy()

     with tempfile.TemporaryDirectory() as tmp_dirname:
         model_a.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase		)

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







@require_tf
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "hf-internal-testing/tiny-random-vit"			,     "hf-internal-testing/tiny-random-bert"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      TFViTModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFBertModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      TFViTModelTester(self		)
     _lowerCAmelCase				=      TFBertModelTester(self		)
     _lowerCAmelCase				=      vit_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      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    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							List[Any]:
     # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
     # just reinitialize it.
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "Rocketknight1/tiny-random-deit-tf"			,     "hf-internal-testing/tiny-random-roberta"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     **_lowerCAmelCase		)    ->							int:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_vision_text_model(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,     text_model=_lowerCAmelCase		)

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

     _lowerCAmelCase				=      output.vision_model_output.attentions
     self.assertEqual(len(_lowerCAmelCase		)			,     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)
     _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 + 2
     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(_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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      TFDeiTModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFRobertaModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      TFDeiTModelTester(self		)
     _lowerCAmelCase				=      TFRobertaModelTester(self		)
     _lowerCAmelCase				=      vit_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      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    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_vision_text_pretrained(
         "Rocketknight1/tiny-random-clip-tf"			,     "hf-internal-testing/tiny-random-bert"		)
     _lowerCAmelCase				=      13
     _lowerCAmelCase				=      floats_tensor(
         [
             batch_size,
             model.vision_model.config.num_channels,
             model.vision_model.config.image_size,
             model.vision_model.config.image_size,
         ]		)
     _lowerCAmelCase				=      ids_tensor([batch_size, 4]			,     model.text_model.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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      TFCLIPVisionModel(_lowerCAmelCase			,     name="vision_model"		)
     _lowerCAmelCase				=      TFBertModel(_lowerCAmelCase			,     name="text_model"		)
     return vision_model, text_model





 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFCLIPVisionModelTester(self		)
     _lowerCAmelCase				=      TFBertModelTester(self		)
     _lowerCAmelCase				=      clip_model_tester.prepare_config_and_inputs()
     _lowerCAmelCase				=      bert_model_tester.prepare_config_and_inputs()

     _lowerCAmelCase			,			_lowerCAmelCase				=      vision_config_and_inputs

     (
         (
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			(
         _lowerCAmelCase
     )			,			
     )				=      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    lowerCAmelCase_		(		unittest.TestCase		):




 @slow
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      TFVisionTextDualEncoderModel.from_pretrained(
         "clip-italian/clip-italian"			,     logit_scale_init_value=1.0			,     from_pt=_lowerCAmelCase		)
     _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=_lowerCAmelCase			,     padding=_lowerCAmelCase			,     return_tensors="np"		)

     _lowerCAmelCase				=      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])			,     )

     _lowerCAmelCase				=      np.array([[1.2284727, 0.3104122]]		)

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


