
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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]				)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=checkpoints.load_tax_checkpoint(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=flatten_dict(UpperCAmelCase_				)
       return flax_params
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						Any:
       SCREAMING_SNAKE_CASE_							:				List[str]		={}

       SCREAMING_SNAKE_CASE_							:				Union[str, 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''',
       }

       SCREAMING_SNAKE_CASE_							:				int		={
           '''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
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''.'''.join(key[1:]				)

                     # rename the key
                     for old, new in CONVERSION_MAPPING.items():
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=new_key.replace(UpperCAmelCase_			,							UpperCAmelCase_				)

                     if "decoder" in new_key:
                            for old, new in DECODER_CONVERSION_MAPPING.items():
                                   SCREAMING_SNAKE_CASE_							:				int		=new_key.replace(UpperCAmelCase_			,							UpperCAmelCase_				)

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

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

                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=flax_dict[key]

       SCREAMING_SNAKE_CASE_							:				List[str]		={}
       # convert converted_dict into torch format
       for key in converted_dict.keys():
              if ("embed_tokens" not in key) and ("embedder" not in key):
                     SCREAMING_SNAKE_CASE_							:				Tuple		=torch.from_numpy(converted_dict[key].T				)
              else:
                     SCREAMING_SNAKE_CASE_							:				int		=torch.from_numpy(converted_dict[key]				)

       return converted_torch_dict
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     List[str]=False			,							UpperCAmelCase_	:     int=False				)			->						List[Any]:
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=get_flax_param(UpperCAmelCase_				)

       if not use_large:
              SCREAMING_SNAKE_CASE_							:				Tuple		=PixaStructVisionConfig()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=PixaStructTextConfig()
       else:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=PixaStructVisionConfig(
                  hidden_size=1_5_3_6			,							d_ff=3_9_6_8			,							num_attention_heads=2_4			,							num_hidden_layers=1_8				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=PixaStructTextConfig(hidden_size=1_5_3_6			,							d_ff=3_9_6_8			,							num_heads=2_4			,							num_layers=1_8				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=PixaStructConfig(
           vision_config=encoder_config.to_dict()			,							text_config=decoder_config.to_dict()			,							is_vqa=UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=PixaStructForConditionalGeneration(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Tuple		=rename_and_convert_flax_params(UpperCAmelCase_				)
       model.load_state_dict(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
       SCREAMING_SNAKE_CASE_							:				int		=PixaStructImageProcessor()
       SCREAMING_SNAKE_CASE_							:				Any		=PixaStructProcessor(image_processor=UpperCAmelCase_			,							tokenizer=UpperCAmelCase_				)

       if use_large:
              SCREAMING_SNAKE_CASE_							:				Dict		=4_0_9_6

       SCREAMING_SNAKE_CASE_							:				Tuple		=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__":
 _lowercase											=    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.""")
 _lowercase											=    parser.parse_args()

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


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

              if hasattr(__A						,       '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
