


import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
       from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image


class   a__	:

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Any=1_3      ,							lowerCamelCase_      :     Tuple=6_4      ,							lowerCamelCase_      :     Any=2      ,							lowerCamelCase_      :     Optional[Any]=3      ,							lowerCamelCase_      :     Any=True      ,							lowerCamelCase_      :     Dict=True      ,							lowerCamelCase_      :     Tuple=3_2      ,							lowerCamelCase_      :     str=5      ,							lowerCamelCase_      :     List[str]=4      ,							lowerCamelCase_      :     Dict=3_7      ,							lowerCamelCase_      :     int="gelu"      ,							lowerCamelCase_      :     Dict=0.1      ,							lowerCamelCase_      :     Any=0.1      ,							lowerCamelCase_      :     List[str]=1_0      ,							lowerCamelCase_      :     int=0.0_2      ,							lowerCamelCase_      :     int=[1, 1_6, 4, 4]      ,							lowerCamelCase_      :     Any=None      ,							):
            a_    :     str										=    parent
            a_    :     Tuple										=    batch_size
            a_    :     List[Any]										=    image_size
            a_    :     Optional[int]										=    patch_size
            a_    :     Union[str, Any]										=    num_channels
            a_    :     Any										=    is_training
            a_    :     List[Any]										=    use_labels
            a_    :     Optional[Any]										=    hidden_size
            a_    :     Optional[int]										=    num_hidden_layers
            a_    :     Optional[int]										=    num_attention_heads
            a_    :     Dict										=    intermediate_size
            a_    :     Any										=    hidden_act
            a_    :     Union[str, Any]										=    hidden_dropout_prob
            a_    :     Optional[int]										=    attention_probs_dropout_prob
            a_    :     Dict										=    type_sequence_label_size
            a_    :     List[str]										=    initializer_range
            a_    :     Union[str, Any]										=    scope
            a_    :     Any										=    backbone_featmap_shape

            # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
            # the number of patches is based on the feature map of the backbone, which by default uses an output stride
            # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
            a_    :     Tuple										=    (self.image_size // 3_2) ** 2
            a_    :     Dict										=    num_patches + 1

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Union[str, Any]										=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

            a_    :     Dict										=    None
            if self.use_labels:
                 a_    :     Tuple										=    ids_tensor([self.batch_size]      ,							self.type_sequence_label_size    )

            a_    :     List[str]										=    self.get_config()

            return config, pixel_values, labels

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Optional[Any]										=    {
                """global_padding""": """same""",
                """layer_type""": """bottleneck""",
                """depths""": [3, 4, 9],
                """out_features""": ["""stage1""", """stage2""", """stage3"""],
                """embedding_dynamic_padding""": True,
                """hidden_sizes""": [4, 8, 1_6, 3_2],
                """num_groups""": 2,
            }

            return ViTHybridConfig(
                image_size=self.image_size      ,							patch_size=self.patch_size      ,							num_channels=self.num_channels      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							is_decoder=lowerCamelCase_      ,							initializer_range=self.initializer_range      ,							backbone_featmap_shape=self.backbone_featmap_shape      ,							backbone_config=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any    ):
            a_    :     List[str]										=    ViTHybridModel(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     int										=    model(lowerCamelCase_    )
            self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     List[str]										=    self.type_sequence_label_size
            a_    :     int										=    ViTHybridForImageClassification(lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Optional[Any]										=    model(lowerCamelCase_      ,							labels=lowerCamelCase_    )
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Any										=    self.prepare_config_and_inputs()
            a_			,			a_			,			a_    :     Optional[Any]										=    config_and_inputs
            a_    :     Optional[int]										=    {"""pixel_values""": pixel_values}
            return config, inputs_dict


@require_torch
class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Optional[Any]							    =     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
       lowerCamelCase__:     str							    =     (
           {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCamelCase__:     List[Any]							    =     False
       lowerCamelCase__:     Optional[Any]							    =     False
       lowerCamelCase__:     Optional[Any]							    =     False

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Dict										=    ViTHybridModelTester(self    )
            a_    :     Any										=    ConfigTester(self      ,							config_class=lowerCamelCase_      ,							has_text_modality=lowerCamelCase_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.config_tester.run_common_tests()

       @unittest.skip(reason="""ViT does not use inputs_embeds"""    )
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            pass

       def 						UpperCAmelCase(   self      :     Any    ):
            a_			,			a_    :     List[Any]										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     List[Any]										=    model_class(lowerCamelCase_    )
                 self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)    )
                 a_    :     List[Any]										=    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCamelCase_      ,							nn.Linear    )    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_			,			a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     List[str]										=    model_class(lowerCamelCase_    )
                 a_    :     Optional[int]										=    inspect.signature(model.forward    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a_    :     Optional[int]										=    [*signature.parameters.keys()]

                 a_    :     Optional[int]										=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Tuple										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     List[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_			,			a_    :     List[Any]										=    self.model_tester.prepare_config_and_inputs_for_common()

            a_    :     Union[str, Any]										=    _config_zero_init(lowerCamelCase_    )
            for model_class in self.all_model_classes:
                 a_    :     Tuple										=    model_class(config=lowerCamelCase_    )
                 # Skip the check for the backbone
                 for name, module in model.named_modules():
                      if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                           a_    :     Optional[int]										=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

                 for name, param in model.named_parameters():
                      if param.requires_grad:
                           if name in backbone_params:
                                continue
                           self.assertIn(
                               ((param.data.mean() * 1E9).round() / 1E9).item()      ,							[0.0, 1.0]      ,							msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,							)

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_    :     str										=    ViTHybridModel.from_pretrained(lowerCamelCase_    )
                 self.assertIsNotNone(lowerCamelCase_    )



def  _a			(						):
     a_    :     Optional[int]										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
     return image


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

       @cached_property
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            return (
                ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
                if is_vision_available()
                else None
            )

       @slow
       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     str										=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
                lowerCamelCase_    )

            a_    :     int										=    self.default_image_processor
            a_    :     List[Any]										=    prepare_img()
            a_    :     str										=    image_processor(images=lowerCamelCase_      ,							return_tensors="""pt"""    ).to(lowerCamelCase_    )

            # forward pass
            with torch.no_grad():
                 a_    :     Tuple										=    model(**lowerCamelCase_    )

            # verify the logits
            a_    :     Tuple										=    torch.Size((1, 1_0_0_0)    )
            self.assertEqual(outputs.logits.shape      ,							lowerCamelCase_    )

            a_    :     List[str]										=    torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]    ).to(lowerCamelCase_    )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							lowerCamelCase_      ,							atol=1E-4    )    )

       @slow
       @require_accelerate
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[Any]										=    ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""    )
            a_    :     str										=    ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""      ,							device_map="""auto"""    )

            a_    :     Dict										=    prepare_img()

            a_    :     Dict										=    image_processor(images=lowerCamelCase_      ,							return_tensors="""pt"""    )
            a_    :     Optional[Any]										=    model(**lowerCamelCase_    )
            a_    :     List[str]										=    outputs.logits
            # model predicts one of the 1000 ImageNet classes
            a_    :     List[str]										=    logits.argmax(-1    ).item()

            self.assertTrue(model.config.idalabel[predicted_class_idx]      ,							"""tabby, tabby cat"""    )







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    )



