






"""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			(__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		checkpoints.load_tax_checkpoint(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		flatten_dict(__a							)
    return flax_params



def 					_A			(__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		{}

    SCREAMING_SNAKE_CASE_      :  Optional[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_      :  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
            SCREAMING_SNAKE_CASE_      :  str    						=		'''.'''.join(key[1:]							)

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

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

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                SCREAMING_SNAKE_CASE_      :  int    						=		re.sub(R'''layers_(\d+)'''    ,				R'''layer.\1'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		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_      :  Any    						=		re.sub(R'''layers_(\d+)'''    ,				R'''layer.\1'''    ,				__a							)

            SCREAMING_SNAKE_CASE_      :  str    						=		flax_dict[key]

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}
    # 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_      :  List[Any]    						=		torch.from_numpy(converted_dict[key].T							)
        else:
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(converted_dict[key]							)

    return converted_torch_dict



def 					_A			(__a    ,				__a    ,				__a=False    ,				__a=False							) ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		get_flax_param(__a							)

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

    SCREAMING_SNAKE_CASE_      :  Dict    						=		PixaStructForConditionalGeneration(__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		rename_and_convert_flax_params(__a							)
    model.load_state_dict(__a							)

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		PixaStructImageProcessor()
    SCREAMING_SNAKE_CASE_      :  str    						=		PixaStructProcessor(image_processor=__a    ,				tokenizer=__a							)

    if use_large:
        SCREAMING_SNAKE_CASE_      :  int    						=		40_96

    SCREAMING_SNAKE_CASE_      :  int    						=		True

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

    model.save_pretrained(__a							)
    processor.save_pretrained(__a							)

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


if __name__ == "__main__":
  UpperCAmelCase_   : 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_   : str   			= parser.parse_args()

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






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,    "False")) is not True     ,    reason="Skipping test because should only be run when releasing minor transformers version"     ,    )
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            if self.framework == "pytorch":
                   subprocess.run(
                       F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,						encoding="utf-8" ,						check=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # creates estimator
            return HuggingFace(
                entry_point=self.script ,						source_dir=self.env.test_path ,						role=self.env.role ,						image_uri=self.env.image_uri ,						base_job_name=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds" ,						9_9_9_9_9_9)
            )

            # assert kpis
            assert train_runtime <= self.results["train_runtime"]
            assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
            assert all(t <= self.results["eval_loss"] for t in eval_loss)

            # dump tests result into json file to share in PR
            with open(F"{estimator.latest_training_job.name}.json" ,						"w") as outfile:
                   json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} ,						__lowerCamelCase)
