




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 			a__    (					snake_case__      ):
						_a							:    List[str]					=    """marian"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    List[str]					=    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(   self		,			_A=5_8_1_0_1		,			_A=None		,			_A=1_0_2_4		,			_A=1_2		,			_A=4_0_9_6		,			_A=1_6		,			_A=1_2		,			_A=4_0_9_6		,			_A=1_6		,			_A=0.0		,			_A=0.0		,			_A=True		,			_A=True		,			_A="gelu"		,			_A=1_0_2_4		,			_A=0.1		,			_A=0.0		,			_A=0.0		,			_A=0.02		,			_A=5_8_1_0_0		,			_A=False		,			_A=5_8_1_0_0		,			_A=0		,			_A=0		,			_A=True		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       decoder_vocab_size or vocab_size
											__lowerCAmelCase				   =       max_position_embeddings
											__lowerCAmelCase				   =       d_model
											__lowerCAmelCase				   =       encoder_ffn_dim
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       encoder_attention_heads
											__lowerCAmelCase				   =       decoder_ffn_dim
											__lowerCAmelCase				   =       decoder_layers
											__lowerCAmelCase				   =       decoder_attention_heads
											__lowerCAmelCase				   =       dropout
											__lowerCAmelCase				   =       attention_dropout
											__lowerCAmelCase				   =       activation_dropout
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       init_std
											__lowerCAmelCase				   =       encoder_layerdrop
											__lowerCAmelCase				   =       decoder_layerdrop
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       encoder_layers
											__lowerCAmelCase				   =       scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase				   =       share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=_A		,			eos_token_id=_A		,			is_encoder_decoder=_A		,			decoder_start_token_id=_A		,			forced_eos_token_id=_A		,			**_A		,			)






class 			a__    (					snake_case__      ):






						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]			)

																if self.use_past:
																					__lowerCAmelCase				   =       {0: "batch"}
																					__lowerCAmelCase				   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
																else:
																					__lowerCAmelCase				   =       {0: "batch", 1: "decoder_sequence"}
																					__lowerCAmelCase				   =       {0: "batch", 1: "decoder_sequence"}

																if self.use_past:
																					self.fill_with_past_key_values_(_A		,			direction="inputs"			)
											elif self.task == "causal-lm":
																# TODO: figure this case out.
																__lowerCAmelCase				   =       OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]			)
																if self.use_past:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																					for i in range(_A			):
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
											else:
																__lowerCAmelCase				   =       OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																    ]			)

											return common_inputs






						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       super().outputs
											else:
																__lowerCAmelCase				   =       super(_A		,			self			).outputs
																if self.use_past:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																					for i in range(_A			):
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
																										__lowerCAmelCase				   =       {0: "batch", 2: "past_sequence + sequence"}
											return common_outputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self._generate_dummy_inputs_for_encoder_and_decoder(
											    _A		,			_A		,			_A		,			_A		,			_A			)

											# Generate decoder inputs
											__lowerCAmelCase				   =       seq_length if not self.use_past else 1
											__lowerCAmelCase				   =       self._generate_dummy_inputs_for_encoder_and_decoder(
											    _A		,			_A		,			_A		,			_A		,			_A			)
											__lowerCAmelCase				   =       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											__lowerCAmelCase				   =       dict(**_A		,			**_A			)

											if self.use_past:
																if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
																else:
																					import torch
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       common_inputs["input_ids"].shape
																__lowerCAmelCase				   =       common_inputs["decoder_input_ids"].shape[1]
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_attention_heads
																__lowerCAmelCase				   =       (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																__lowerCAmelCase				   =       decoder_seq_length + 3
																__lowerCAmelCase				   =       (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																__lowerCAmelCase				   =       torch.cat(
																    [common_inputs["decoder_attention_mask"], torch.ones(_A		,			_A			)]		,			dim=1			)

																__lowerCAmelCase				   =       []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																__lowerCAmelCase				   =       min(_A		,			_A			)
																__lowerCAmelCase				   =       max(_A		,			_A			) - min_num_layers
																__lowerCAmelCase				   =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																for _ in range(_A			):
																					common_inputs["past_key_values"].append(
																					    (
																					        torch.zeros(_A			),
																					        torch.zeros(_A			),
																					        torch.zeros(_A			),
																					        torch.zeros(_A			),
																					    )			)
																# TODO: test this.
																__lowerCAmelCase				   =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
																for _ in range(_A		,			_A			):
																					common_inputs["past_key_values"].append((torch.zeros(_A			), torch.zeros(_A			))			)
											return common_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self._generate_dummy_inputs_for_encoder_and_decoder(
											    _A		,			_A		,			_A		,			_A		,			_A			)

											if self.use_past:
																if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
																else:
																					import torch
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase				   =       seqlen + 2
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_layers
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.num_attention_heads
																__lowerCAmelCase				   =       (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																__lowerCAmelCase				   =       common_inputs["attention_mask"].dtype
																__lowerCAmelCase				   =       torch.cat(
																    [common_inputs["attention_mask"], torch.ones(_A		,			_A		,			dtype=_A			)]		,			dim=1			)
																__lowerCAmelCase				   =       [
																    (torch.zeros(_A			), torch.zeros(_A			)) for _ in range(_A			)
																]
											return common_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       compute_effective_axis_dimension(
											    _A		,			fixed_dimension=OnnxConfig.default_fixed_batch		,			num_token_to_add=0			)

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__lowerCAmelCase				   =       tokenizer.num_special_tokens_to_add(_A			)
											__lowerCAmelCase				   =       compute_effective_axis_dimension(
											    _A		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=_A			)

											# Generate dummy inputs according to compute batch and sequence
											__lowerCAmelCase				   =       [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
											__lowerCAmelCase				   =       dict(tokenizer(_A		,			return_tensors=_A			)			)
											return common_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = -1		,			_A = -1		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _A		,			batch_size=_A		,			seq_length=_A		,			is_pair=_A		,			framework=_A			)

											else:
																__lowerCAmelCase				   =       self._generate_dummy_inputs_for_causal_lm(
																    _A		,			batch_size=_A		,			seq_length=_A		,			is_pair=_A		,			framework=_A			)

											return common_inputs






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase				   =       super()._flatten_past_key_values_(_A		,			_A		,			_A		,			_A			)
											else:
																__lowerCAmelCase				   =       super(_A		,			self			)._flatten_past_key_values_(
																    _A		,			_A		,			_A		,			_A			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return 1E-4










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_							:				Union[str, Any]     ):
					if isinstance(SCREAMING_SNAKE_CASE_      ,      collections.abc.Iterable     ):
										return x
					return (x, x)
@require_tf
class 			a__    :






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       VisionTextDualEncoderConfig.from_vision_text_configs(_A		,			_A			)

											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel(_A			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_vision_text_model(_A		,			_A			)
											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel(vision_model=_A		,			text_model=_A			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_vision_text_model(_A		,			_A			)
											__lowerCAmelCase				   =       {"vision_model": vision_model, "text_model": text_model}
											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_A			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_vision_text_model(_A		,			_A			)
											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel(vision_model=_A		,			text_model=_A			)

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

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

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_vision_text_model(_A		,			_A			)
											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel(vision_model=_A		,			text_model=_A			)

											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			pixel_values=_A		,			attention_mask=_A		,			output_attentions=_A			)

											__lowerCAmelCase				   =       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)
											__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(_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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       np.abs((a - b)			).max()
											self.assertLessEqual(_A		,			_A		,			f"""Difference between torch and flax is {diff} (>= {tol})."""			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_model(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											self.check_model_from_pretrained_configs(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_from_pretrained(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											self.check_save_load(**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											self.check_vision_text_output_attention(**_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_pretrained_model_and_inputs()

											__lowerCAmelCase				   =       model_a(**_A			)
											__lowerCAmelCase				   =       outputs[0].numpy()

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

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






@require_tf
class 			a__    (					snake_case__ ,				unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
											    "hf-internal-testing/tiny-random-vit"		,			"hf-internal-testing/tiny-random-bert"			)
											__lowerCAmelCase				   =       1_3
											__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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFViTModel(_A		,			name="vision_model"			)
											__lowerCAmelCase				   =       TFBertModel(_A		,			name="text_model"			)
											return vision_model, text_model






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__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 			a__    (					snake_case__ ,				unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
											    "Rocketknight1/tiny-random-deit-tf"		,			"hf-internal-testing/tiny-random-roberta"			)
											__lowerCAmelCase				   =       1_3
											__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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.get_vision_text_model(_A		,			_A			)
											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel(vision_model=_A		,			text_model=_A			)

											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			pixel_values=_A		,			attention_mask=_A		,			output_attentions=_A			)

											__lowerCAmelCase				   =       output.vision_model_output.attentions
											self.assertEqual(len(_A			)		,			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(_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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFDeiTModel(_A		,			name="vision_model"			)
											__lowerCAmelCase				   =       TFRobertaModel(_A		,			name="text_model"			)
											return vision_model, text_model






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__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 			a__    (					snake_case__ ,				unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
											    "Rocketknight1/tiny-random-clip-tf"		,			"hf-internal-testing/tiny-random-bert"			)
											__lowerCAmelCase				   =       1_3
											__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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFCLIPVisionModel(_A		,			name="vision_model"			)
											__lowerCAmelCase				   =       TFBertModel(_A		,			name="text_model"			)
											return vision_model, text_model






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__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 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFVisionTextDualEncoderModel.from_pretrained(
											    "clip-italian/clip-italian"		,			logit_scale_init_value=1.0		,			from_pt=_A			)
											__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=_A		,			padding=_A		,			return_tensors="np"			)

											__lowerCAmelCase				   =       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])		,			)

											__lowerCAmelCase				   =       np.array([[1.2_28_47_27, 0.3_10_41_22]]			)

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




