'''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       lowerCAmelCase_							(   snake_case_			:					Optional[int]     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  checkpoints.load_tax_checkpoint(snake_case_     )
     UpperCAmelCase_			       =  flatten_dict(snake_case_     )
     return flax_params





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}

     UpperCAmelCase_			       =  {
         "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",
     }

     UpperCAmelCase_			       =  {
         "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
               UpperCAmelCase_			       =  ".".join(key[1:]     )

               # rename the key
               for old, new in CONVERSION_MAPPING.items():
                    UpperCAmelCase_			       =  new_key.replace(snake_case_   ,       snake_case_     )

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

               if "layers" in new_key and "decoder" not in new_key:
                    # use regex to replace the layer number
                    UpperCAmelCase_			       =  re.sub(R"layers_(\d+)"   ,       R"layer.\1"   ,       snake_case_     )
                    UpperCAmelCase_			       =  new_key.replace("encoder"   ,       "encoder.encoder"     )

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

               UpperCAmelCase_			       =  flax_dict[key]

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

     return converted_torch_dict





def       lowerCAmelCase_							(   snake_case_			:					Tuple   ,       snake_case_			:					Dict   ,       snake_case_			:					Dict=False   ,       snake_case_			:					Tuple=False     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  get_flax_param(snake_case_     )

     if not use_large:
          UpperCAmelCase_			       =  PixaStructVisionConfig()
          UpperCAmelCase_			       =  PixaStructTextConfig()
     else:
          UpperCAmelCase_			       =  PixaStructVisionConfig(
              hidden_size=15_36   ,       d_ff=39_68   ,       num_attention_heads=24   ,       num_hidden_layers=18     )
          UpperCAmelCase_			       =  PixaStructTextConfig(hidden_size=15_36   ,       d_ff=39_68   ,       num_heads=24   ,       num_layers=18     )
     UpperCAmelCase_			       =  PixaStructConfig(
         vision_config=encoder_config.to_dict()   ,       text_config=decoder_config.to_dict()   ,       is_vqa=snake_case_     )

     UpperCAmelCase_			       =  PixaStructForConditionalGeneration(snake_case_     )

     UpperCAmelCase_			       =  rename_and_convert_flax_params(snake_case_     )
     model.load_state_dict(snake_case_     )

     UpperCAmelCase_			       =  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"     )
     UpperCAmelCase_			       =  PixaStructImageProcessor()
     UpperCAmelCase_			       =  PixaStructProcessor(image_processor=snake_case_   ,       tokenizer=snake_case_     )

     if use_large:
          UpperCAmelCase_			       =  40_96

     UpperCAmelCase_			       =  True

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

     model.save_pretrained(snake_case_     )
     processor.save_pretrained(snake_case_     )

     print("Model saved in {}".format(snake_case_     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	List[str]    =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.')
    SCREAMING_SNAKE_CASE_:	int    =parser.parse_args()

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











'''simple docstring'''




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
							from transformers import MarkupLMFeatureExtractor




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			parent



					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							return {}







def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			"""<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR=\"FFFFFF\">
    <HR>
    <a href=\"http://google.com\">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style=\"color:#0000FF\">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>"""

		SCREAMING_SNAKE_CASE :				int		=			"""
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    """

		return [html_string_a, html_string_a]




@require_bsa
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	MarkupLMFeatureExtractor if is_bsa_available() else None


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			MarkupLMFeatureExtractionTester(self						)


					@property
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							return self.feature_extract_tester.prepare_feat_extract_dict()



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.feature_extraction_class()

							# Test not batched input
							SCREAMING_SNAKE_CASE :				Optional[int]		=			get_html_strings()[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
							SCREAMING_SNAKE_CASE :				List[str]		=			[["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
							# fmt: on

							self.assertEqual(encoding.nodes      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)

							# Test batched
							SCREAMING_SNAKE_CASE :				str		=			get_html_strings()
							SCREAMING_SNAKE_CASE :				str		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				int		=			expected_nodes + [["""My First Heading""", """My first paragraph."""]]
							SCREAMING_SNAKE_CASE :				List[Any]		=			expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]

							self.assertEqual(len(encoding.nodes						)      ,						2						)
							self.assertEqual(len(encoding.xpaths						)      ,						2						)

							self.assertEqual(encoding.nodes      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)



