





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							_UpperCAmelCase       (       UpperCAmelCase__       ):

  """simple docstring"""


  a_              =					["""image_processor""", """tokenizer"""]
  a_              =					"""Pix2StructImageProcessor"""
  a_              =					("""T5Tokenizer""", """T5TokenizerFast""")






  def __init__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						str	)				->		int:
     __lowerCAmelCase				      =      False
     super().__init__(__lowercase   ,    __lowercase	)






  def __call__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any=None   ,    lowerCAmelCase_					:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,    lowerCAmelCase_					:						bool = True   ,    lowerCAmelCase_					:						Union[bool, str, PaddingStrategy] = False   ,    lowerCAmelCase_					:						Union[bool, str, TruncationStrategy] = None   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						Optional[int] = 2_0_4_8   ,    lowerCAmelCase_					:						int = 0   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						Optional[bool] = None   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = False   ,    lowerCAmelCase_					:						bool = True   ,    lowerCAmelCase_					:						Optional[Union[str, TensorType]] = None   ,    **lowerCAmelCase_					:						Optional[Any]   ,    )				->		int:






     if images is None and text is None:
        raise ValueError('You have to specify either images or text.'	)

     # Get only text
     if images is None and not self.image_processor.is_vqa:
        __lowerCAmelCase				      =      self.tokenizer
        __lowerCAmelCase				      =      self.tokenizer(
            text=__lowercase   ,    add_special_tokens=__lowercase   ,    padding=__lowercase   ,    truncation=__lowercase   ,    max_length=__lowercase   ,    stride=__lowercase   ,    pad_to_multiple_of=__lowercase   ,    return_attention_mask=__lowercase   ,    return_overflowing_tokens=__lowercase   ,    return_special_tokens_mask=__lowercase   ,    return_offsets_mapping=__lowercase   ,    return_token_type_ids=__lowercase   ,    return_length=__lowercase   ,    verbose=__lowercase   ,    return_tensors=__lowercase   ,    **__lowercase   ,    )
        return text_encoding

     if not self.image_processor.is_vqa:
        # add pixel_values
        __lowerCAmelCase				      =      self.image_processor(
            __lowercase   ,    return_tensors=__lowercase   ,    max_patches=__lowercase   ,    **__lowercase	)
     else:
        # add pixel_values and bbox
        __lowerCAmelCase				      =      self.image_processor(
            __lowercase   ,    return_tensors=__lowercase   ,    max_patches=__lowercase   ,    header_text=__lowercase   ,    **__lowercase	)

     if text is not None and not self.image_processor.is_vqa:
        __lowerCAmelCase				      =      self.tokenizer(
            text=__lowercase   ,    add_special_tokens=__lowercase   ,    padding=__lowercase   ,    truncation=__lowercase   ,    max_length=__lowercase   ,    stride=__lowercase   ,    pad_to_multiple_of=__lowercase   ,    return_attention_mask=__lowercase   ,    return_overflowing_tokens=__lowercase   ,    return_special_tokens_mask=__lowercase   ,    return_offsets_mapping=__lowercase   ,    return_token_type_ids=__lowercase   ,    return_length=__lowercase   ,    verbose=__lowercase   ,    return_tensors=__lowercase   ,    **__lowercase   ,    )

        if "attention_mask" in text_encoding:
           __lowerCAmelCase				      =      text_encoding.pop('attention_mask'	)
        if "input_ids" in text_encoding:
           __lowerCAmelCase				      =      text_encoding.pop('input_ids'	)
     else:
        __lowerCAmelCase				      =      None

     if text_encoding is not None:
        encoding_image_processor.update(__lowercase	)

     return encoding_image_processor






  def 							lowercase						(							self					:						List[str]   ,    *lowerCAmelCase_					:						Optional[int]   ,    **lowerCAmelCase_					:						List[str]	)				->		Tuple:
     return self.tokenizer.batch_decode(*__lowercase   ,    **__lowercase	)






  def 							lowercase						(							self					:						Any   ,    *lowerCAmelCase_					:						Optional[int]   ,    **lowerCAmelCase_					:						Tuple	)				->		str:
     return self.tokenizer.decode(*__lowercase   ,    **__lowercase	)






  @property
  def 							lowercase						(							self					:						Union[str, Any]	)				->		int:
     __lowerCAmelCase				      =      self.tokenizer.model_input_names
     __lowerCAmelCase				      =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)







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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]     )
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							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=__lowercase			,      )
							assert hasattr(self			,      "env"		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	{
							    "enabled": True,
							    "processes_per_host": 8,
							}
							snake_case_						       =	{
							    "enabled": True,
							    "parameters": {
							        "microbatches": 4,
							        "placement_strategy": "spread",
							        "pipeline": "interleaved",
							        "optimize": "speed",
							        "partitions": 4,
							        "ddp": True,
							    },
							}

							snake_case_						       =	{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

							snake_case_						       =	"trainer" if self.script == "run_glue.py" else "smtrainer"
							# 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=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"			,      instance_count=__lowercase			,      instance_type=self.instance_type			,      debugger_hook_config=__lowercase			,      hyperparameters={
							        **self.env.hyperparameters,
							        "model_name_or_path": self.model_name_or_path,
							        "max_steps": 5_00,
							    }			,      metric_definitions=self.env.metric_definitions			,      distribution=__lowercase			,      py_version="py36"			,      )






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(1,)]		)
						def 			snake_case__		(		self				:	Any			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

							# result dataframe
							snake_case_						       =	TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"			,      99_99_99		)
							)

							# 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}			,      __lowercase		)
