



import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""image_processor""", """tokenizer"""]
					lowercase_								=		"""OwlViTImageProcessor"""
					lowercase_								=		("""CLIPTokenizer""", """CLIPTokenizerFast""")







					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]=None				,       SCREAMING_SNAKE_CASE :     Any=None				,       **SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   Union[str, Any]													=					None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."				,       SCREAMING_SNAKE_CASE				,       )
													lowercase__					:   Union[str, Any]													=					kwargs.pop("feature_extractor"					)

									lowercase__					:   List[str]													=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."					)
									if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."					)

									super().__init__(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def __call__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     Any="max_length"				,       SCREAMING_SNAKE_CASE :     str="np"				,       **SCREAMING_SNAKE_CASE :     Any					):

									if text is None and query_images is None and images is None:
													raise ValueError(
													    "You have to specify at least one text or query image or image. All three cannot be none."					)

									if text is not None:
													if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) or (isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) and not isinstance(text[0]				,       SCREAMING_SNAKE_CASE					)):
																	lowercase__					:   Union[str, Any]													=					[self.tokenizer(SCREAMING_SNAKE_CASE				,       padding=SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)]

													elif isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) and isinstance(text[0]				,       SCREAMING_SNAKE_CASE					):
																	lowercase__					:   Dict													=					[]

																	# Maximum number of queries across batch
																	lowercase__					:   Optional[Any]													=					max([len(SCREAMING_SNAKE_CASE					) for t in text]					)

																	# Pad all batch samples to max number of text queries
																	for t in text:
																					if len(SCREAMING_SNAKE_CASE					) != max_num_queries:
																									lowercase__					:   Optional[Any]													=					t + [" "] * (max_num_queries - len(SCREAMING_SNAKE_CASE					))

																					lowercase__					:   Optional[Any]													=					self.tokenizer(SCREAMING_SNAKE_CASE				,       padding=SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
																					encodings.append(SCREAMING_SNAKE_CASE					)
													else:
																	raise TypeError("Input text should be a string, a list of strings or a nested list of strings"					)

													if return_tensors == "np":
																	lowercase__					:   Tuple													=					np.concatenate([encoding["input_ids"] for encoding in encodings]				,       axis=0					)
																	lowercase__					:   List[Any]													=					np.concatenate([encoding["attention_mask"] for encoding in encodings]				,       axis=0					)

													elif return_tensors == "jax" and is_flax_available():
																	import jax.numpy as jnp

																	lowercase__					:   int													=					jnp.concatenate([encoding["input_ids"] for encoding in encodings]				,       axis=0					)
																	lowercase__					:   Dict													=					jnp.concatenate([encoding["attention_mask"] for encoding in encodings]				,       axis=0					)

													elif return_tensors == "pt" and is_torch_available():
																	import torch

																	lowercase__					:   Any													=					torch.cat([encoding["input_ids"] for encoding in encodings]				,       dim=0					)
																	lowercase__					:   Optional[Any]													=					torch.cat([encoding["attention_mask"] for encoding in encodings]				,       dim=0					)

													elif return_tensors == "tf" and is_tf_available():
																	import tensorflow as tf

																	lowercase__					:   Union[str, Any]													=					tf.stack([encoding["input_ids"] for encoding in encodings]				,       axis=0					)
																	lowercase__					:   Dict													=					tf.stack([encoding["attention_mask"] for encoding in encodings]				,       axis=0					)

													else:
																	raise ValueError("Target return tensor type could not be returned"					)

													lowercase__					:   Optional[Any]													=					BatchEncoding()
													lowercase__					:   List[str]													=					input_ids
													lowercase__					:   Tuple													=					attention_mask

									if query_images is not None:
													lowercase__					:   Dict													=					BatchEncoding()
													lowercase__					:   List[Any]													=					self.image_processor(
													    SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					).pixel_values
													lowercase__					:   List[str]													=					query_pixel_values

									if images is not None:
													lowercase__					:   Optional[int]													=					self.image_processor(SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									if text is not None and images is not None:
													lowercase__					:   Optional[Any]													=					image_features.pixel_values
													return encoding
									elif query_images is not None and images is not None:
													lowercase__					:   List[str]													=					image_features.pixel_values
													return encoding
									elif text is not None or query_images is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE					)				,       tensor_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]				,       *SCREAMING_SNAKE_CASE :     Dict				,       **SCREAMING_SNAKE_CASE :     List[str]					):
									return self.image_processor.post_process(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       *SCREAMING_SNAKE_CASE :     Tuple				,       **SCREAMING_SNAKE_CASE :     Dict					):
									return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]				,       *SCREAMING_SNAKE_CASE :     Any				,       **SCREAMING_SNAKE_CASE :     Tuple					):
									return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]				,       *SCREAMING_SNAKE_CASE :     List[str]				,       **SCREAMING_SNAKE_CASE :     Any					):
									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[Any]				,       *SCREAMING_SNAKE_CASE :     Any				,       **SCREAMING_SNAKE_CASE :     int					):
									return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					@property
					def 			snake_case					(							self :     str					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,       SCREAMING_SNAKE_CASE				,       )
									return self.image_processor_class







					@property
					def 			snake_case					(							self :     Tuple					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,       SCREAMING_SNAKE_CASE				,       )
									return self.image_processor









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

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, Any]					):
									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=SCREAMING_SNAKE_CASE				,       )
									assert hasattr(self				,       "env"					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=1					):
									# 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}-single"""				,       instance_count=SCREAMING_SNAKE_CASE				,       instance_type=self.instance_type				,       debugger_hook_config=SCREAMING_SNAKE_CASE				,       hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}				,       metric_definitions=self.env.metric_definitions				,       py_version="py36"				,       )







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   Any													=					(
									    Session().describe_training_job(estimator.latest_training_job.name					).get("TrainingTimeInSeconds"				,       999_999					)
									)

									# 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}				,       SCREAMING_SNAKE_CASE					)




