





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_					(			__a						)	->						str:







  """simple docstring"""





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



@require_tf
class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  int)			->List[str]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   pass

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->List[Any]:



   '''simple docstring'''



   pass

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Any=None			,					**UpperCAmelCase_							:  List[str])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =VisionTextDualEncoderConfig.from_vision_text_configs(a_			,					a_)

   lowerCamelCase__: Dict         =TFVisionTextDualEncoderModel(a_)

   lowerCamelCase__: Optional[int]         =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							:  str			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  List[str]=None			,					**UpperCAmelCase_							:  str)			->str:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.get_vision_text_model(a_			,					a_)
   lowerCamelCase__: Dict         =TFVisionTextDualEncoderModel(vision_model=a_			,					text_model=a_)

   lowerCamelCase__: Union[str, Any]         =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							:  str			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  Dict)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: int         =self.get_vision_text_model(a_			,					a_)
   lowerCamelCase__: List[str]         ={'vision_model': vision_model, 'text_model': text_model}
   lowerCamelCase__: Any         =TFVisionTextDualEncoderModel.from_vision_text_pretrained(**a_)

   lowerCamelCase__: Dict         =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							:  Union[str, Any]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Tuple=None			,					**UpperCAmelCase_							:  str)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.get_vision_text_model(a_			,					a_)
   lowerCamelCase__: List[Any]         =TFVisionTextDualEncoderModel(vision_model=a_			,					text_model=a_)

   lowerCamelCase__: Dict         =model(input_ids=a_			,					pixel_values=a_			,					attention_mask=a_)
   lowerCamelCase__: List[str]         =output[0].numpy()

   with tempfile.TemporaryDirectory() as tmpdirname:
     model.save_pretrained(a_)
     lowerCamelCase__: List[str]         =TFVisionTextDualEncoderModel.from_pretrained(a_)

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]=None			,					**UpperCAmelCase_							:  Optional[int])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.get_vision_text_model(a_			,					a_)
   lowerCamelCase__: Tuple         =TFVisionTextDualEncoderModel(vision_model=a_			,					text_model=a_)

   lowerCamelCase__: str         =model(
       input_ids=a_			,					pixel_values=a_			,					attention_mask=a_			,					output_attentions=a_)

   lowerCamelCase__: Union[str, Any]         =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__: Optional[int]         =to_atuple(vision_model.config.image_size)
   lowerCamelCase__: int         =to_atuple(vision_model.config.patch_size)
   lowerCamelCase__: Optional[int]         =(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__: str         =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							:  int			,					UpperCAmelCase_							:  np.ndarray			,					UpperCAmelCase_							:  np.ndarray			,					UpperCAmelCase_							:  float)			->List[str]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.prepare_config_and_inputs()
   self.check_vision_text_dual_encoder_model(**a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Dict:



   '''simple docstring'''



   lowerCamelCase__: int         =self.prepare_config_and_inputs()
   self.check_model_from_pretrained_configs(**a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->int:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.prepare_config_and_inputs()
   self.check_vision_text_dual_encoder_from_pretrained(**a_)

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



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.prepare_config_and_inputs()
   self.check_save_load(**a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.prepare_config_and_inputs()
   self.check_vision_text_output_attention(**a_)







 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->int:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_pretrained_model_and_inputs()

   lowerCamelCase__: int         =model_a(**a_)
   lowerCamelCase__: Dict         =outputs[0].numpy()

   with tempfile.TemporaryDirectory() as tmp_dirname:
     model_a.save_pretrained(a_)
     lowerCamelCase__: int         =TFVisionTextDualEncoderModel.from_pretrained(a_)

     lowerCamelCase__: int         =model_a(**a_)
     lowerCamelCase__: Union[str, Any]         =after_outputs[0].numpy()
     lowerCamelCase__: Dict         =np.amax(np.abs(out_a - out_a))
     self.assertLessEqual(a_			,					1E-5)



@require_tf
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Dict:



   '''simple docstring'''



   lowerCamelCase__: str         =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
       "hf-internal-testing/tiny-random-vit"			,					"hf-internal-testing/tiny-random-bert")
   lowerCamelCase__: Any         =13
   lowerCamelCase__: List[Any]         =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__: Tuple         =random_attention_mask([batch_size, 4])
   lowerCamelCase__: Tuple         ={'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

   return model, inputs

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =TFViTModel(a_			,					name="vision_model")
   lowerCamelCase__: Union[str, Any]         =TFBertModel(a_			,					name="text_model")
   return vision_model, text_model







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =TFViTModelTester(self)
   lowerCamelCase__: Tuple         =TFBertModelTester(self)
   lowerCamelCase__: Union[str, Any]         =vit_model_tester.prepare_config_and_inputs()
   lowerCamelCase__: List[str]         =bert_model_tester.prepare_config_and_inputs()

   lowerCamelCase__: List[Any]         =vision_config_and_inputs

   (
       lowerCamelCase__  
   ): Optional[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		(      __SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
       "Rocketknight1/tiny-random-deit-tf"			,					"hf-internal-testing/tiny-random-roberta")
   lowerCamelCase__: str         =13
   lowerCamelCase__: List[str]         =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__: List[Any]         =random_attention_mask([batch_size, 4])
   lowerCamelCase__: Optional[int]         ={'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

   return model, inputs

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  Dict=None			,					**UpperCAmelCase_							:  Tuple)			->str:



   '''simple docstring'''



   lowerCamelCase__: str         =self.get_vision_text_model(a_			,					a_)
   lowerCamelCase__: Optional[Any]         =TFVisionTextDualEncoderModel(vision_model=a_			,					text_model=a_)

   lowerCamelCase__: Union[str, Any]         =model(
       input_ids=a_			,					pixel_values=a_			,					attention_mask=a_			,					output_attentions=a_)

   lowerCamelCase__: str         =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__: Dict         =to_atuple(vision_model.config.image_size)
   lowerCamelCase__: List[Any]         =to_atuple(vision_model.config.patch_size)
   lowerCamelCase__: Tuple         =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   lowerCamelCase__: Optional[int]         =num_patches + 2
   self.assertEqual(vision_attentions[0].shape[-3:]			,					(vision_config.num_attention_heads, seq_len, seq_len))

   lowerCamelCase__: int         =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							:  Tuple			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Tuple)			->str:



   '''simple docstring'''



   lowerCamelCase__: Dict         =TFDeiTModel(a_			,					name="vision_model")
   lowerCamelCase__: Optional[int]         =TFRobertaModel(a_			,					name="text_model")
   return vision_model, text_model







 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Dict:



   '''simple docstring'''



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

   lowerCamelCase__: List[str]         =vision_config_and_inputs

   (
       lowerCamelCase__  
   ): Dict         =text_config_and_inputs

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



@require_tf
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->Tuple:



   '''simple docstring'''



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

   return model, inputs

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Union[str, Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =TFCLIPVisionModel(a_			,					name="vision_model")
   lowerCamelCase__: Any         =TFBertModel(a_			,					name="text_model")
   return vision_model, text_model







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



   '''simple docstring'''



   lowerCamelCase__: int         =TFCLIPVisionModelTester(self)
   lowerCamelCase__: str         =TFBertModelTester(self)
   lowerCamelCase__: Dict         =clip_model_tester.prepare_config_and_inputs()
   lowerCamelCase__: Dict         =bert_model_tester.prepare_config_and_inputs()

   lowerCamelCase__: str         =vision_config_and_inputs

   (
       lowerCamelCase__  
   ): Optional[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_vision
@require_tf
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__: Any         =TFVisionTextDualEncoderModel.from_pretrained(
       "clip-italian/clip-italian"			,					logit_scale_init_value=1.0			,					from_pt=a_)
   lowerCamelCase__: Any         =VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")

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

   lowerCamelCase__: Union[str, Any]         =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__: str         =np.array([[1.228_4727, 0.310_4122]])

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






'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




