


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ImageGPTImageProcessor


class   a__	(						unittest.TestCase    ):

       def __init__(   self      :     int      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Union[str, Any]=7      ,							lowerCamelCase_      :     Optional[Any]=3      ,							lowerCamelCase_      :     str=1_8      ,							lowerCamelCase_      :     Union[str, Any]=3_0      ,							lowerCamelCase_      :     str=4_0_0      ,							lowerCamelCase_      :     Optional[Any]=True      ,							lowerCamelCase_      :     int=None      ,							lowerCamelCase_      :     Tuple=True      ,							):
            a_    :     Optional[Any]										=    size if size is not None else {"""height""": 1_8, """width""": 1_8}
            a_    :     Dict										=    parent
            a_    :     List[str]										=    batch_size
            a_    :     Optional[int]										=    num_channels
            a_    :     Dict										=    image_size
            a_    :     Union[str, Any]										=    min_resolution
            a_    :     str										=    max_resolution
            a_    :     Any										=    do_resize
            a_    :     str										=    size
            a_    :     Any										=    do_normalize

       def 						UpperCAmelCase(   self      :     Tuple    ):
            return {
                # here we create 2 clusters for the sake of simplicity
                "clusters": np.asarray(
                    [
                        [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                        [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                    ]    ),
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
            }


@require_torch
@require_vision
class   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Optional[Any]							    =     ImageGPTImageProcessor if is_vision_available() else None

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     int										=    ImageGPTImageProcessingTester(self    )

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     str										=    self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(lowerCamelCase_      ,							"""clusters"""    )    )
            self.assertTrue(hasattr(lowerCamelCase_      ,							"""do_resize"""    )    )
            self.assertTrue(hasattr(lowerCamelCase_      ,							"""size"""    )    )
            self.assertTrue(hasattr(lowerCamelCase_      ,							"""do_normalize"""    )    )

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Union[str, Any]										=    self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size      ,							{"""height""": 1_8, """width""": 1_8}    )

            a_    :     Union[str, Any]										=    self.image_processing_class.from_dict(self.image_processor_dict      ,							size=4_2    )
            self.assertEqual(image_processor.size      ,							{"""height""": 4_2, """width""": 4_2}    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Dict										=    self.image_processing_class(**self.image_processor_dict    )
            a_    :     Any										=    json.loads(image_processor.to_json_string()    )
            for key, value in self.image_processor_dict.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCamelCase_      ,							obj[key]    )    )
                 else:
                      self.assertEqual(obj[key]      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     str										=    self.image_processing_class(**self.image_processor_dict    )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_    :     Dict										=    os.path.join(lowerCamelCase_      ,							"""image_processor.json"""    )
                 image_processor_first.to_json_file(lowerCamelCase_    )
                 a_    :     int										=    self.image_processing_class.from_json_file(lowerCamelCase_    ).to_dict()

            a_    :     List[Any]										=    image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCamelCase_      ,							image_processor_second[key]    )    )
                 else:
                      self.assertEqual(image_processor_first[key]      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     int										=    self.image_processing_class(**self.image_processor_dict    )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 image_processor_first.save_pretrained(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.image_processing_class.from_pretrained(lowerCamelCase_    ).to_dict()

            a_    :     Tuple										=    image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCamelCase_      ,							image_processor_second[key]    )    )
                 else:
                      self.assertEqual(image_processor_first[key]      ,							lowerCamelCase_    )

       @unittest.skip("""ImageGPT requires clusters at initialization"""    )
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            pass



def  _a			(						):
     a_    :     Tuple										=    load_dataset("""hf-internal-testing/fixtures_image_utils"""  ,  split="""test""" )

     a_    :     List[Any]										=    Image.open(dataset[4]["""file"""] )
     a_    :     Optional[Any]										=    Image.open(dataset[5]["""file"""] )

     a_    :     List[str]										=    [imagea, imagea]

     return images


@require_vision
@require_torch
class   a__	(						unittest.TestCase    ):

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Dict										=    ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""    )

            a_    :     List[Any]										=    prepare_images()

            # test non-batched
            a_    :     Union[str, Any]										=    image_processing(images[0]      ,							return_tensors="""pt"""    )

            self.assertIsInstance(encoding.input_ids      ,							torch.LongTensor    )
            self.assertEqual(encoding.input_ids.shape      ,							(1, 1_0_2_4)    )

            a_    :     Dict										=    [3_0_6, 1_9_1, 1_9_1]
            self.assertEqual(encoding.input_ids[0, :3].tolist()      ,							lowerCamelCase_    )

            # test batched
            a_    :     Dict										=    image_processing(lowerCamelCase_      ,							return_tensors="""pt"""    )

            self.assertIsInstance(encoding.input_ids      ,							torch.LongTensor    )
            self.assertEqual(encoding.input_ids.shape      ,							(2, 1_0_2_4)    )

            a_    :     Optional[int]										=    [3_0_3, 1_3, 1_3]
            self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,							lowerCamelCase_    )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



