





"""simple docstring"""





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
							raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
							raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      'Hello, World!'
_UpperCamelCase							=      'en_XX'



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							bool   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =Path('''data_bin'''   )
							__lowerCamelCase      :      List[str]         =FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(SCREAMING_SNAKE_CASE   ).parent   )    ,						checkpoint_file=Path(SCREAMING_SNAKE_CASE   ).name    ,						_name='''xmod_base'''    ,						arch='''xmod_base'''    ,						task='''multilingual_masked_lm'''    ,						data_name_or_path=str(SCREAMING_SNAKE_CASE   )    ,						bpe='''sentencepiece'''    ,						sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE   ).parent / '''sentencepiece.bpe.model'''   )    ,						src_dict=str(data_dir / '''dict.txt'''   )    ,						)
							xmod.eval()  # disable dropout
							print(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Union[str, Any]         =xmod.model.encoder.sentence_encoder
							__lowerCamelCase      :      List[str]         =XmodConfig(
							    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings    ,						hidden_size=xmod.cfg.model.encoder_embed_dim    ,						num_hidden_layers=xmod.cfg.model.encoder_layers    ,						num_attention_heads=xmod.cfg.model.encoder_attention_heads    ,						intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim    ,						max_position_embeddings=514    ,						type_vocab_size=1    ,						layer_norm_eps=1E-5    ,						pre_norm=xmod.cfg.model.encoder_normalize_before    ,						adapter_reduction_factor=getattr(xmod.cfg.model    ,						'''bottleneck'''    ,						2   )    ,						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm    ,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm    ,						ln_before_adapter=xmod.cfg.model.ln_before_adapter    ,						languages=xmod.cfg.model.languages    ,						)
							if classification_head:
														__lowerCamelCase      :      str         =xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our X-MOD config:'''    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[Any]         =XmodForSequenceClassification(SCREAMING_SNAKE_CASE   ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE   )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							__lowerCamelCase      :      Dict         =xmod_sent_encoder.embed_tokens.weight
							__lowerCamelCase      :      Optional[Any]         =xmod_sent_encoder.embed_positions.weight
							__lowerCamelCase      :      Union[str, Any]         =torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c xmod doesn't use them.

							__lowerCamelCase      :      Optional[Any]         =xmod_sent_encoder.layernorm_embedding.weight
							__lowerCamelCase      :      Any         =xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers   ):
														# Encoder: start of layer
														__lowerCamelCase      :      Optional[Any]         =model.roberta.encoder.layer[i]
														__lowerCamelCase      :      Optional[Any]         =xmod_sent_encoder.layers[i]

														# self attention
														__lowerCamelCase      :      int         =layer.attention.self
														if not (
														    xmod_layer.self_attn.k_proj.weight.data.shape
														    == xmod_layer.self_attn.q_proj.weight.data.shape
														    == xmod_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)   )
														):
																					raise AssertionError('''Dimensions of self-attention weights do not match.'''   )

														__lowerCamelCase      :      Optional[Any]         =xmod_layer.self_attn.q_proj.weight
														__lowerCamelCase      :      List[Any]         =xmod_layer.self_attn.q_proj.bias
														__lowerCamelCase      :      str         =xmod_layer.self_attn.k_proj.weight
														__lowerCamelCase      :      List[Any]         =xmod_layer.self_attn.k_proj.bias
														__lowerCamelCase      :      Optional[int]         =xmod_layer.self_attn.v_proj.weight
														__lowerCamelCase      :      List[Any]         =xmod_layer.self_attn.v_proj.bias

														# self-attention output
														__lowerCamelCase      :      Optional[Any]         =layer.attention.output
														if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																					raise AssertionError('''Dimensions of self-attention output weights do not match.'''   )
														__lowerCamelCase      :      List[str]         =xmod_layer.self_attn.out_proj.weight
														__lowerCamelCase      :      Optional[int]         =xmod_layer.self_attn.out_proj.bias
														__lowerCamelCase      :      int         =xmod_layer.self_attn_layer_norm.weight
														__lowerCamelCase      :      Union[str, Any]         =xmod_layer.self_attn_layer_norm.bias

														# intermediate
														__lowerCamelCase      :      List[Any]         =layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of intermediate weights do not match.'''   )
														__lowerCamelCase      :      List[str]         =xmod_layer.fca.weight
														__lowerCamelCase      :      str         =xmod_layer.fca.bias

														# output
														__lowerCamelCase      :      Optional[int]         =layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of feed-forward weights do not match.'''   )
														__lowerCamelCase      :      Dict         =xmod_layer.fca.weight
														__lowerCamelCase      :      List[str]         =xmod_layer.fca.bias
														__lowerCamelCase      :      List[str]         =xmod_layer.final_layer_norm.weight
														__lowerCamelCase      :      Dict         =xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					__lowerCamelCase      :      List[str]         =xmod_layer.adapter_layer_norm.weight
																					__lowerCamelCase      :      List[str]         =xmod_layer.adapter_layer_norm.bias

														if sorted(bert_output.adapter_modules.keys()   ) != sorted(xmod_layer.adapter_modules.keys()   ):
																					raise AssertionError('''Lists of language adapters do not match.'''   )
														for lang_code, adapter in xmod_layer.adapter_modules.items():
																					__lowerCamelCase      :      List[str]         =bert_output.adapter_modules[lang_code]
																					__lowerCamelCase      :      Tuple         =xmod_layer.adapter_modules[lang_code]
																					__lowerCamelCase      :      Tuple         =from_adapter.fca.weight
																					__lowerCamelCase      :      Dict         =from_adapter.fca.bias
																					__lowerCamelCase      :      str         =from_adapter.fca.weight
																					__lowerCamelCase      :      int         =from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														__lowerCamelCase      :      Optional[int]         =xmod_sent_encoder.layer_norm.weight
														__lowerCamelCase      :      Optional[Any]         =xmod_sent_encoder.layer_norm.bias

							if classification_head:
														__lowerCamelCase      :      Tuple         =xmod.model.classification_heads['''mnli'''].dense.weight
														__lowerCamelCase      :      List[Any]         =xmod.model.classification_heads['''mnli'''].dense.bias
														__lowerCamelCase      :      Any         =xmod.model.classification_heads['''mnli'''].out_proj.weight
														__lowerCamelCase      :      List[str]         =xmod.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														__lowerCamelCase      :      Optional[int]         =xmod.model.encoder.lm_head.dense.weight
														__lowerCamelCase      :      Tuple         =xmod.model.encoder.lm_head.dense.bias
														__lowerCamelCase      :      List[str]         =xmod.model.encoder.lm_head.layer_norm.weight
														__lowerCamelCase      :      int         =xmod.model.encoder.lm_head.layer_norm.bias
														__lowerCamelCase      :      str         =xmod.model.encoder.lm_head.weight
														__lowerCamelCase      :      int         =xmod.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							__lowerCamelCase      :      Tuple         =xmod.encode(SCREAMING_SNAKE_CASE   ).unsqueeze(0   )  # batch of size 1
							model.roberta.set_default_language(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =model(SCREAMING_SNAKE_CASE   )[0]
							if classification_head:
														__lowerCamelCase      :      Tuple         =xmod.model.classification_heads['''mnli'''](xmod.extract_features(SCREAMING_SNAKE_CASE   )   )
							else:
														__lowerCamelCase      :      str         =xmod.model(SCREAMING_SNAKE_CASE    ,						lang_id=[SAMPLE_LANGUAGE]   )[0]
							print(our_output.shape    ,						their_output.shape   )
							__lowerCamelCase      :      Union[str, Any]         =torch.max(torch.abs(our_output - their_output   )   ).item()
							print(F'max_absolute_diff = {max_absolute_diff}'   )  # ~ 1e-7
							__lowerCamelCase      :      Optional[Any]         =torch.allclose(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-3   )
							print('''Do both models output the same tensors?'''    ,						'''🔥''' if success else '''💩'''   )
							if not success:
														raise Exception('''Something went wRoNg'''   )

							Path(SCREAMING_SNAKE_CASE   ).mkdir(parents=SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
							print(F'Saving model to {pytorch_dump_folder_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)











"""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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							if isinstance(SCREAMING_SNAKE_CASE    ,						collections.abc.Iterable   ):
														return x
							return (x, x)
@require_tf
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :Union[str, Any]						,    __lowercase  :List[Any]			):
											pass




				def 					__lowercase  (     self  :Any			):
											pass




				def 					__lowercase  (     self  :str			):
											pass




				def 					__lowercase  (     self  :str						,    __lowercase  :Dict						,    __lowercase  :List[Any]						,    __lowercase  :Union[str, Any]						,    __lowercase  :Union[str, Any]						,    __lowercase  :Any=None						,    **__lowercase  :Tuple			):
											__lowerCamelCase      :      Optional[Any]         =VisionTextDualEncoderConfig.from_vision_text_configs(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      Dict         =TFVisionTextDualEncoderModel(__lowercase			)

											__lowerCamelCase      :      List[Any]         =model(input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase			)

											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 					__lowercase  (     self  :str						,    __lowercase  :Any						,    __lowercase  :Dict						,    __lowercase  :Any						,    __lowercase  :int						,    __lowercase  :str=None						,    **__lowercase  :List[str]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Any         =self.get_vision_text_model(__lowercase						,    __lowercase			)
											__lowerCamelCase      :      List[str]         =TFVisionTextDualEncoderModel(vision_model=__lowercase						,    text_model=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =model(input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase			)

											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 					__lowercase  (     self  :int						,    __lowercase  :Dict						,    __lowercase  :List[Any]						,    __lowercase  :Any						,    __lowercase  :Optional[Any]						,    __lowercase  :Tuple=None						,    **__lowercase  :List[Any]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      int         =self.get_vision_text_model(__lowercase						,    __lowercase			)
											__lowerCamelCase      :      Dict         ={'''vision_model''': vision_model, '''text_model''': text_model}
											__lowerCamelCase      :      List[Any]         =TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowercase			)

											__lowerCamelCase      :      Optional[Any]         =model(input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase			)

											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 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :Dict						,    __lowercase  :int						,    __lowercase  :Dict						,    __lowercase  :List[Any]						,    __lowercase  :Tuple=None						,    **__lowercase  :Optional[Any]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =self.get_vision_text_model(__lowercase						,    __lowercase			)
											__lowerCamelCase      :      List[Any]         =TFVisionTextDualEncoderModel(vision_model=__lowercase						,    text_model=__lowercase			)

											__lowerCamelCase      :      Any         =model(input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =output[0].numpy()

											with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(__lowercase			)
																		__lowerCamelCase      :      Dict         =TFVisionTextDualEncoderModel.from_pretrained(__lowercase			)

																		__lowerCamelCase      :      Optional[Any]         =model(input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase			)
																		__lowerCamelCase      :      Any         =after_output[0].numpy()
																		__lowerCamelCase      :      Tuple         =np.amax(np.abs(out_a - out_a			)			)
																		self.assertLessEqual(__lowercase						,    1e-5			)




				def 					__lowercase  (     self  :int						,    __lowercase  :Any						,    __lowercase  :List[Any]						,    __lowercase  :Any						,    __lowercase  :str						,    __lowercase  :int=None						,    **__lowercase  :List[Any]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Any         =self.get_vision_text_model(__lowercase						,    __lowercase			)
											__lowerCamelCase      :      Union[str, Any]         =TFVisionTextDualEncoderModel(vision_model=__lowercase						,    text_model=__lowercase			)

											__lowerCamelCase      :      Any         =model(
											    input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase						,    output_attentions=__lowercase			)

											__lowerCamelCase      :      Optional[Any]         =output.vision_model_output.attentions
											self.assertEqual(len(__lowercase			)						,    vision_config.num_hidden_layers			)

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

											__lowerCamelCase      :      List[Any]         =output.text_model_output.attentions
											self.assertEqual(len(__lowercase			)						,    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 					__lowercase  (     self  :List[Any]						,    __lowercase  :np.ndarray						,    __lowercase  :np.ndarray						,    __lowercase  :float			):
											__lowerCamelCase      :      Dict         =np.abs((a - b)			).max()
											self.assertLessEqual(__lowercase						,    __lowercase						,    f'Difference between torch and flax is {diff} (>= {tol}).'			)




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      Any         =self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_model(**__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[Any]         =self.prepare_config_and_inputs()
											self.check_model_from_pretrained_configs(**__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      Dict         =self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_from_pretrained(**__lowercase			)




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      List[Any]         =self.prepare_config_and_inputs()
											self.check_save_load(**__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      Any         =self.prepare_config_and_inputs()
											self.check_vision_text_output_attention(**__lowercase			)




				@slow
				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Optional[Any]         =self.get_pretrained_model_and_inputs()

											__lowerCamelCase      :      Optional[Any]         =model_a(**__lowercase			)
											__lowerCamelCase      :      int         =outputs[0].numpy()

											with tempfile.TemporaryDirectory() as tmp_dirname:
																		model_a.save_pretrained(__lowercase			)
																		__lowerCamelCase      :      List[Any]         =TFVisionTextDualEncoderModel.from_pretrained(__lowercase			)

																		__lowerCamelCase      :      Tuple         =model_a(**__lowercase			)
																		__lowerCamelCase      :      Optional[int]         =after_outputs[0].numpy()
																		__lowerCamelCase      :      Optional[Any]         =np.amax(np.abs(out_a - out_a			)			)
																		self.assertLessEqual(__lowercase						,    1e-5			)

@require_tf
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Union[str, Any]         =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
											    '''hf-internal-testing/tiny-random-vit'''						,    '''hf-internal-testing/tiny-random-bert'''			)
											__lowerCamelCase      :      Any         =13
											__lowerCamelCase      :      Optional[int]         =floats_tensor(
											    [
											        batch_size,
											        model.vision_model.config.num_channels,
											        model.vision_model.config.image_size,
											        model.vision_model.config.image_size,
											    ]			)
											__lowerCamelCase      :      Dict         =ids_tensor([batch_size, 4]						,    model.text_model.config.vocab_size			)
											__lowerCamelCase      :      Optional[Any]         =random_attention_mask([batch_size, 4]			)
											__lowerCamelCase      :      Dict         ={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

											return model, inputs




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :List[Any]						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      Optional[Any]         =TFViTModel(__lowercase						,    name='''vision_model'''			)
											__lowerCamelCase      :      Union[str, Any]         =TFBertModel(__lowercase						,    name='''text_model'''			)
											return vision_model, text_model




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      str         =TFViTModelTester(self			)
											__lowerCamelCase      :      Any         =TFBertModelTester(self			)
											__lowerCamelCase      :      Optional[Any]         =vit_model_tester.prepare_config_and_inputs()
											__lowerCamelCase      :      Tuple         =bert_model_tester.prepare_config_and_inputs()

											__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =vision_config_and_inputs

											(
											    (
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						
											)      :      int         =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_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""




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

											return model, inputs




				def 					__lowercase  (     self  :Any						,    __lowercase  :Tuple						,    __lowercase  :List[Any]						,    __lowercase  :Union[str, Any]						,    __lowercase  :Any						,    __lowercase  :int=None						,    **__lowercase  :List[str]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Union[str, Any]         =self.get_vision_text_model(__lowercase						,    __lowercase			)
											__lowerCamelCase      :      Optional[int]         =TFVisionTextDualEncoderModel(vision_model=__lowercase						,    text_model=__lowercase			)

											__lowerCamelCase      :      Any         =model(
											    input_ids=__lowercase						,    pixel_values=__lowercase						,    attention_mask=__lowercase						,    output_attentions=__lowercase			)

											__lowerCamelCase      :      str         =output.vision_model_output.attentions
											self.assertEqual(len(__lowercase			)						,    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      :      int         =to_atuple(vision_model.config.image_size			)
											__lowerCamelCase      :      Union[str, Any]         =to_atuple(vision_model.config.patch_size			)
											__lowerCamelCase      :      Tuple         =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
											__lowerCamelCase      :      Any         =num_patches + 2
											self.assertEqual(vision_attentions[0].shape[-3:]						,    (vision_config.num_attention_heads, seq_len, seq_len)			)

											__lowerCamelCase      :      Any         =output.text_model_output.attentions
											self.assertEqual(len(__lowercase			)						,    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 					__lowercase  (     self  :Any						,    __lowercase  :Any						,    __lowercase  :Optional[Any]			):
											__lowerCamelCase      :      str         =TFDeiTModel(__lowercase						,    name='''vision_model'''			)
											__lowerCamelCase      :      List[str]         =TFRobertaModel(__lowercase						,    name='''text_model'''			)
											return vision_model, text_model




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Optional[int]         =TFDeiTModelTester(self			)
											__lowerCamelCase      :      Any         =TFRobertaModelTester(self			)
											__lowerCamelCase      :      Dict         =vit_model_tester.prepare_config_and_inputs()
											__lowerCamelCase      :      Optional[int]         =bert_model_tester.prepare_config_and_inputs()

											__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =vision_config_and_inputs

											(
											    (
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						
											)      :      List[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_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""




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

											return model, inputs




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :str						,    __lowercase  :Tuple			):
											__lowerCamelCase      :      int         =TFCLIPVisionModel(__lowercase						,    name='''vision_model'''			)
											__lowerCamelCase      :      Optional[Any]         =TFBertModel(__lowercase						,    name='''text_model'''			)
											return vision_model, text_model




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[str]         =TFCLIPVisionModelTester(self			)
											__lowerCamelCase      :      Union[str, Any]         =TFBertModelTester(self			)
											__lowerCamelCase      :      str         =clip_model_tester.prepare_config_and_inputs()
											__lowerCamelCase      :      Tuple         =bert_model_tester.prepare_config_and_inputs()

											__lowerCamelCase    ,						__lowerCamelCase      :      Any         =vision_config_and_inputs

											(
											    (
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						(
											    __lowerCamelCase
											)    ,						
											)      :      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_vision
@require_tf
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				@slow
				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Union[str, Any]         =TFVisionTextDualEncoderModel.from_pretrained(
											    '''clip-italian/clip-italian'''						,    logit_scale_init_value=1.0						,    from_pt=__lowercase			)
											__lowerCamelCase      :      str         =VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''			)

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

											__lowerCamelCase      :      str         =model(**__lowercase			)

											# 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      :      int         =np.array([[1.2284727, 0.3104122]]			)

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




