



'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


snake_case_ :	Optional[int]         =  [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]






def A__    (     UpperCAmelCase_							):
						for pegasus_name, hf_name in PATTERNS:
												_UpperCamelCase					:		Optional[Any] 				=   k.replace(UpperCAmelCase_  ,  UpperCAmelCase_							)
						return k






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		List[Any] 				=   DEFAULTS.copy()
						cfg_kwargs.update(UpperCAmelCase_							)
						_UpperCamelCase					:		Any 				=   PegasusConfig(**UpperCAmelCase_							)
						_UpperCamelCase					:		int 				=   PegasusForConditionalGeneration(UpperCAmelCase_							)
						_UpperCamelCase					:		int 				=   torch_model.model.state_dict()
						_UpperCamelCase					:		Optional[int] 				=   {}
						for k, v in tf_weights.items():
												_UpperCamelCase					:		Optional[int] 				=   rename_state_dict_key(UpperCAmelCase_							)
												if new_k not in sd:
																		raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'							)

												if "dense" in k or "proj" in new_k:
																		_UpperCamelCase					:		int 				=   v.T
												_UpperCamelCase					:		List[str] 				=   torch.tensor(UpperCAmelCase_  ,  dtype=sd[new_k].dtype							)
												assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
						# make sure embedding.padding_idx is respected
						_UpperCamelCase					:		List[Any] 				=   torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]							)
						_UpperCamelCase					:		Any 				=   mapping['shared.weight']
						_UpperCamelCase					:		Optional[Any] 				=   mapping['shared.weight']
						_UpperCamelCase					:		str 				=   {k: torch.zeros_like(UpperCAmelCase_							) for k, v in sd.items() if k.endswith('bias'							) and k not in mapping}
						mapping.update(**UpperCAmelCase_							)
						_UpperCamelCase				,			_UpperCamelCase					:		int 				=   torch_model.model.load_state_dict(UpperCAmelCase_  ,  strict=UpperCAmelCase_							)
						_UpperCamelCase					:		Optional[Any] 				=   [
						    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
						]
						assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
						assert extra == [], f'no matches found for the following tf keys {extra}'
						return torch_model






def A__    (     UpperCAmelCase_="./ckpt/aeslc/model.ckpt-32000"							):
						_UpperCamelCase					:		str 				=   tf.train.list_variables(UpperCAmelCase_							)
						_UpperCamelCase					:		int 				=   {}
						_UpperCamelCase					:		Optional[int] 				=   ['Adafactor', 'global_step']
						for name, shape in tqdm(UpperCAmelCase_  ,  desc='converting tf checkpoint to dict'							):
												_UpperCamelCase					:		str 				=   any(pat in name for pat in ignore_name							)
												if skip_key:
																		continue
												_UpperCamelCase					:		List[str] 				=   tf.train.load_variable(UpperCAmelCase_  ,  UpperCAmelCase_							)
												_UpperCamelCase					:		Tuple 				=   array
						return tf_weights






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						# save tokenizer first
						_UpperCamelCase					:		Optional[int] 				=   Path(UpperCAmelCase_							).parent.name
						_UpperCamelCase					:		Dict 				=   task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
						_UpperCamelCase					:		Tuple 				=   PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,  model_max_length=UpperCAmelCase_							)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(UpperCAmelCase_							)

						# convert model
						_UpperCamelCase					:		Optional[Any] 				=   get_tf_weights_as_numpy(UpperCAmelCase_							)
						_UpperCamelCase					:		Optional[Any] 				=   task_specific_params[f'summarization_{dataset}']
						if dataset == "large":
												_UpperCamelCase					:		Optional[int] 				=   task_specific_params
						_UpperCamelCase					:		int 				=   convert_pegasus(UpperCAmelCase_  ,  UpperCAmelCase_							)
						torch_model.save_pretrained(UpperCAmelCase_							)
						_UpperCamelCase					:		List[str] 				=   torch_model.state_dict()
						sd.pop('model.decoder.embed_positions.weight'							)
						sd.pop('model.encoder.embed_positions.weight'							)
						torch.save(UpperCAmelCase_  ,  Path(UpperCAmelCase_							) / 'pytorch_model.bin'							)


if __name__ == "__main__":
					snake_case_ :	List[Any]         =  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
					parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
					snake_case_ :	Optional[int]         =  parser.parse_args()
					if args.save_dir is None:
										snake_case_ :	List[str]         =  Path(args.tf_ckpt_path).parent.name
										snake_case_ :	Optional[int]         =  os.path.join('pegasus', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
 from transformers import (
     FlaxBertModel,
     FlaxCLIPVisionModel,
     FlaxVisionTextDualEncoderModel,
     FlaxViTModel,
     VisionTextDualEncoderConfig,
     VisionTextDualEncoderProcessor,
 )
 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )


if is_torch_available():
 import torch

 from transformers import VisionTextDualEncoderModel

if is_vision_available():
 from PIL import Image



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Optional[int]:
    if isinstance(lowerCamelCase_							, collections.abc.Iterable ):
        return x
    return (x, x)






@require_flax
class _lowerCamelCase:
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase       :							str      		=		np.abs((a - b)).max()
           self.assertLessEqual(lowerCamelCase,      lowerCamelCase,      F'''Difference between torch and flax is {diff} (>= {tol}).''')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      **lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Any      		=		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		FlaxVisionTextDualEncoderModel(lowerCamelCase)

           _lowercase       :							Any      		=		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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      **lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Union[str, Any]      		=		self.get_vision_text_model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							str      		=		{'vision_model': vision_model, 'text_model': text_model}
           _lowercase       :							Dict      		=		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase)

           _lowercase       :							List[str]      		=		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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      **lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Tuple      		=		self.get_vision_text_model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		{'vision_model': vision_model, 'text_model': text_model}
           _lowercase       :							Dict      		=		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase)

           _lowercase       :							List[str]      		=		model(input_ids=lowerCamelCase,      pixel_values=lowerCamelCase,      attention_mask=lowerCamelCase)
           _lowercase       :							Tuple      		=		output[0]

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowerCamelCase)
               _lowercase       :							Any      		=		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase)

               _lowercase       :							Tuple      		=		model(input_ids=lowerCamelCase,      pixel_values=lowerCamelCase,      attention_mask=lowerCamelCase)
               _lowercase       :							str      		=		after_output[0]
               _lowercase       :							Optional[Any]      		=		np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(lowerCamelCase,      1E-3)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      **lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Any      		=		self.get_vision_text_model(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							Optional[int]      		=		{'vision_model': vision_model, 'text_model': text_model}
           _lowercase       :							Dict      		=		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase)

           _lowercase       :							Tuple      		=		model(
               input_ids=lowerCamelCase,      pixel_values=lowerCamelCase,      attention_mask=lowerCamelCase,      output_attentions=lowerCamelCase)

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

           _lowercase       :							List[str]      		=		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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           pt_model.to(lowerCamelCase)
           pt_model.eval()

           # prepare inputs
           _lowercase       :							Any      		=		inputs_dict
           _lowercase       :							Optional[int]      		=		{k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

           with torch.no_grad():
               _lowercase       :							Tuple      		=		pt_model(**lowerCamelCase).to_tuple()

           _lowercase       :							Any      		=		fx_model(**lowerCamelCase).to_tuple()
           self.assertEqual(len(lowerCamelCase),      len(lowerCamelCase),      'Output lengths differ between Flax and PyTorch')
           for fx_output, pt_output in zip(fx_outputs[:4],      pt_outputs[:4]):
               self.assert_almost_equals(lowerCamelCase,      pt_output.numpy(),      4E-2)

           # PT -> Flax
           with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(lowerCamelCase)
               _lowercase       :							int      		=		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase,      from_pt=lowerCamelCase)

           _lowercase       :							List[Any]      		=		fx_model_loaded(**lowerCamelCase).to_tuple()
           self.assertEqual(len(lowerCamelCase),      len(lowerCamelCase),      'Output lengths differ between Flax and PyTorch')
           for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],      pt_outputs[:4]):
               self.assert_almost_equals(lowerCamelCase,      pt_output.numpy(),      4E-2)

           # Flax -> PT
           with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(lowerCamelCase)
               _lowercase       :							List[Any]      		=		VisionTextDualEncoderModel.from_pretrained(lowerCamelCase,      from_flax=lowerCamelCase)

           pt_model_loaded.to(lowerCamelCase)
           pt_model_loaded.eval()

           with torch.no_grad():
               _lowercase       :							Optional[Any]      		=		pt_model_loaded(**lowerCamelCase).to_tuple()

           self.assertEqual(len(lowerCamelCase),      len(lowerCamelCase),      'Output lengths differ between Flax and PyTorch')
           for fx_output, pt_output_loaded in zip(fx_outputs[:4],      pt_outputs_loaded[:4]):
               self.assert_almost_equals(lowerCamelCase,      pt_output_loaded.numpy(),      4E-2)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		VisionTextDualEncoderModel(lowerCamelCase)
           _lowercase       :							str      		=		FlaxVisionTextDualEncoderModel(lowerCamelCase)

           _lowercase       :							Tuple      		=		convert_pytorch_state_dict_to_flax(pt_model.state_dict(),      lowerCamelCase)
           _lowercase       :							List[Any]      		=		fx_state

           self.check_pt_flax_equivalence(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							Tuple      		=		VisionTextDualEncoderModel(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		FlaxVisionTextDualEncoderModel(lowerCamelCase)

           _lowercase       :							List[str]      		=		load_flax_weights_in_pytorch_model(lowerCamelCase,      fx_model.params)

           self.check_pt_flax_equivalence(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.prepare_config_and_inputs()
           self.check_save_load(**lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**lowerCamelCase)
       @is_pt_flax_cross_test
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.prepare_config_and_inputs()
           _lowercase       :							List[str]      		=		config_inputs_dict.pop('vision_config')
           _lowercase       :							str      		=		config_inputs_dict.pop('text_config')

           _lowercase       :							int      		=		config_inputs_dict

           self.check_equivalence_pt_to_flax(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           self.check_equivalence_flax_to_pt(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)




       @slow
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase		,				_lowercase       :							Optional[Any]      		=		self.get_pretrained_model_and_inputs()

           _lowercase       :							Optional[int]      		=		model_a(**lowerCamelCase)
           _lowercase       :							Tuple      		=		outputs[0]

           with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(lowerCamelCase)
               _lowercase       :							int      		=		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase)

               _lowercase       :							List[Any]      		=		model_a(**lowerCamelCase)
               _lowercase       :							Tuple      		=		after_outputs[0]
               _lowercase       :							Dict      		=		np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(lowerCamelCase,      1E-5)






@require_flax
class _lowerCamelCase(						_a, unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               'hf-internal-testing/tiny-random-vit',      'hf-internal-testing/tiny-bert',      vision_from_pt=lowerCamelCase,      text_from_pt=lowerCamelCase,      )
           _lowercase       :							List[Any]      		=		13
           _lowercase       :							str      		=		floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ])
           _lowercase       :							Tuple      		=		ids_tensor([batch_size, 4],      model.config.text_config.vocab_size)
           _lowercase       :							Union[str, Any]      		=		random_attention_mask([batch_size, 4])
           _lowercase       :							int      		=		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

           return model, inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		FlaxViTModel(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		FlaxBertModel(lowerCamelCase)
           return vision_model, text_model




       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		FlaxViTModelTester(self)
           _lowercase       :							Any      		=		FlaxBertModelTester(self)
           _lowercase       :							Dict      		=		vit_model_tester.prepare_config_and_inputs()
           _lowercase       :							Any      		=		bert_model_tester.prepare_config_and_inputs()

           _lowercase		,				_lowercase       :							List[str]      		=		vision_config_and_inputs

           _lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							Tuple      		=		text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }






@require_torch
class _lowerCamelCase(						_a, unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							str      		=		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               'hf-internal-testing/tiny-random-clip',      'hf-internal-testing/tiny-bert',      vision_from_pt=lowerCamelCase,      text_from_pt=lowerCamelCase,      )
           _lowercase       :							Tuple      		=		13
           _lowercase       :							Any      		=		floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ])
           _lowercase       :							Union[str, Any]      		=		ids_tensor([batch_size, 4],      model.config.text_config.vocab_size)
           _lowercase       :							Any      		=		random_attention_mask([batch_size, 4])
           _lowercase       :							Dict      		=		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

           return model, inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Any      		=		FlaxCLIPVisionModel(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		FlaxBertModel(lowerCamelCase)
           return vision_model, text_model




       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		FlaxCLIPVisionModelTester(self)
           _lowercase       :							Union[str, Any]      		=		FlaxBertModelTester(self)
           _lowercase       :							Tuple      		=		clip_model_tester.prepare_config_and_inputs()
           _lowercase       :							str      		=		bert_model_tester.prepare_config_and_inputs()

           _lowercase		,				_lowercase       :							Dict      		=		vision_config_and_inputs

           _lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							Optional[int]      		=		text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }






@require_flax
@require_vision
class _lowerCamelCase(						unittest.TestCase   ):
       @slow
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian',      logit_scale_init_value=1.0)
           _lowercase       :							List[str]      		=		VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian')

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

           _lowercase       :							List[Any]      		=		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]),      )

           _lowercase       :							Optional[int]      		=		np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]])

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


