





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]:
    lowerCamelCase__    :      Dict					 =		checkpoints.load_tax_checkpoint(_UpperCAmelCase )
    lowerCamelCase__    :      Union[str, Any]					 =		flatten_dict(_UpperCAmelCase )
    return flax_params


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		{}

    lowerCamelCase__    :      List[str]					 =		{
        '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',
    }

    lowerCamelCase__    :      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
            lowerCamelCase__    :      Union[str, Any]					 =		'.'.join(key[1:] )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                lowerCamelCase__    :      str					 =		new_key.replace(_UpperCAmelCase						,	_UpperCAmelCase )

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

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                lowerCamelCase__    :      Tuple					 =		re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	_UpperCAmelCase )
                lowerCamelCase__    :      Dict					 =		new_key.replace('encoder'						,	'encoder.encoder' )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                lowerCamelCase__    :      str					 =		re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	_UpperCAmelCase )

            lowerCamelCase__    :      Optional[int]					 =		flax_dict[key]

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

    return converted_torch_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=False )			->     List[str]:
    lowerCamelCase__    :      Any					 =		get_flax_param(_UpperCAmelCase )

    if not use_large:
        lowerCamelCase__    :      str					 =		PixaStructVisionConfig()
        lowerCamelCase__    :      Dict					 =		PixaStructTextConfig()
    else:
        lowerCamelCase__    :      Dict					 =		PixaStructVisionConfig(
            hidden_size=1536						,	d_ff=3968						,	num_attention_heads=24						,	num_hidden_layers=18 )
        lowerCamelCase__    :      Optional[Any]					 =		PixaStructTextConfig(hidden_size=1536						,	d_ff=3968						,	num_heads=24						,	num_layers=18 )
    lowerCamelCase__    :      Any					 =		PixaStructConfig(
        vision_config=encoder_config.to_dict()						,	text_config=decoder_config.to_dict()						,	is_vqa=_UpperCAmelCase )

    lowerCamelCase__    :      Union[str, Any]					 =		PixaStructForConditionalGeneration(_UpperCAmelCase )

    lowerCamelCase__    :      Tuple					 =		rename_and_convert_flax_params(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )

    lowerCamelCase__    :      str					 =		AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer' )
    lowerCamelCase__    :      List[str]					 =		PixaStructImageProcessor()
    lowerCamelCase__    :      Dict					 =		PixaStructProcessor(image_processor=_UpperCAmelCase						,	tokenizer=_UpperCAmelCase )

    if use_large:
        lowerCamelCase__    :      Any					 =		4096

    lowerCamelCase__    :      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__":
    _UpperCAmelCase				:       Optional[Any]	   =				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.""")
    _UpperCAmelCase				:       Optional[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"""






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


_lowercase : Union[str, Any]      						=						["text", "image", "audio"]






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=[]

			for input_type in input_types:
						if input_type == "text":
									inputs.append('''Text input'''	)
						elif input_type == "image":
									inputs.append(
									    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
						elif input_type == "audio":
									inputs.append(torch.ones(3000	)	)
						elif isinstance(__lowerCamelCase							,		__lowerCamelCase	):
									inputs.append(create_inputs(__lowerCamelCase	)	)
						else:
									raise ValueError(f'''Invalid type requested: {input_type}'''	)

			return inputs






def      snake_case__   (		__lowerCamelCase				:    List	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[]

			for output in outputs:
						if isinstance(__lowerCamelCase							,		(str, AgentText)	):
									output_types.append('''text'''	)
						elif isinstance(__lowerCamelCase							,		(Image.Image, AgentImage)	):
									output_types.append('''image'''	)
						elif isinstance(__lowerCamelCase							,		(torch.Tensor, AgentAudio)	):
									output_types.append('''audio'''	)
						else:
									raise ValueError(f'''Invalid output: {output}'''	)

			return output_types

@is_tool_test
class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								self.assertTrue(hasattr(self.tool,  '''inputs'''     )     )
								self.assertTrue(hasattr(self.tool,  '''outputs'''     )     )

								lowerCamelCase__		:		Tuple			=self.tool.inputs
								for _input in inputs:
											if isinstance(_input,  lowerCamelCase     ):
														for __input in _input:
																	self.assertTrue(__input in authorized_types     )
											else:
														self.assertTrue(_input in authorized_types     )

								lowerCamelCase__		:		Optional[Any]			=self.tool.outputs
								for _output in outputs:
											self.assertTrue(_output in authorized_types     )



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		List[Any]			=self.tool(*lowerCamelCase     )

								# There is a single output
								if len(self.tool.outputs     ) == 1:
											lowerCamelCase__		:		Optional[int]			=[outputs]

								self.assertListEqual(output_types(lowerCamelCase     ),  self.tool.outputs     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								self.assertTrue(hasattr(self.tool,  '''description'''     )     )
								self.assertTrue(hasattr(self.tool,  '''default_checkpoint'''     )     )
								self.assertTrue(self.tool.description.startswith('''This is a tool that'''     )     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					str:
								lowerCamelCase__		:		List[str]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		Optional[Any]			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Any			=[outputs]

								self.assertEqual(len(lowerCamelCase     ),  len(self.tool.outputs     )     )

								for output, output_type in zip(lowerCamelCase,  self.tool.outputs     ):
											lowerCamelCase__		:		List[Any]			=AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(lowerCamelCase,  lowerCamelCase     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=create_inputs(self.tool.inputs     )

								lowerCamelCase__		:		List[str]			=[]

								for _input, input_type in zip(lowerCamelCase,  self.tool.inputs     ):
											if isinstance(lowerCamelCase,  lowerCamelCase     ):
														_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input     ) for _input_type in input_type]     )
											else:
														_inputs.append(AGENT_TYPE_MAPPING[input_type](_input     )     )

        # Should not raise an error
								lowerCamelCase__		:		Any			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Optional[int]			=[outputs]

								self.assertEqual(len(lowerCamelCase     ),  len(self.tool.outputs     )     )




