




'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a    :							Optional[Any]										=    {'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class 		a							(  unittest.TestCase	):
				snake_case_           =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				snake_case_           =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

				if model_mapping is not None:
								snake_case_           =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
				if tf_model_mapping is not None:
								snake_case_           =						{
								    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
								}


				@require_torch
				def     A_					(						self       :   Dict   ):
									snake_case_		     =							pipeline(
									    task='''text-classification'''			,							model='''hf-internal-testing/tiny-random-distilbert'''			,							framework='''pt'''   )

									snake_case_		     =							text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									snake_case_		     =							text_classifier('''This is great !'''			,							top_k=2   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]   )

									snake_case_		     =							text_classifier(['''This is great !''', '''This is bad''']			,							top_k=2   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									    ]			,							)

									snake_case_		     =							text_classifier('''This is great !'''			,							top_k=1   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									# Legacy behavior
									snake_case_		     =							text_classifier('''This is great !'''			,							return_all_scores=lowercase_   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )

									snake_case_		     =							text_classifier('''This is great !'''			,							return_all_scores=lowercase_   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]   )

									snake_case_		     =							text_classifier(['''This is great !''', '''Something else''']			,							return_all_scores=lowercase_   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
									    ]			,							)

									snake_case_		     =							text_classifier(['''This is great !''', '''Something else''']			,							return_all_scores=lowercase_   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[
									        {'''label''': '''LABEL_0''', '''score''': 0.504},
									        {'''label''': '''LABEL_0''', '''score''': 0.504},
									    ]			,							)


				@require_torch
				def     A_					(						self       :   Tuple   ):
									import torch

									snake_case_		     =							pipeline(
									    task='''text-classification'''			,							model='''hf-internal-testing/tiny-random-distilbert'''			,							framework='''pt'''			,							device=torch.device('''cpu'''   )			,							)

									snake_case_		     =							text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )


				@require_tf
				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							pipeline(
									    task='''text-classification'''			,							model='''hf-internal-testing/tiny-random-distilbert'''			,							framework='''tf'''   )

									snake_case_		     =							text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''LABEL_0''', '''score''': 0.504}]   )


				@slow
				@require_torch
				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							pipeline('''text-classification'''   )

									snake_case_		     =							text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
									snake_case_		     =							text_classifier('''This is bad !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
									snake_case_		     =							text_classifier('''Birds are a type of animal'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''POSITIVE''', '''score''': 0.988}]   )


				@slow
				@require_tf
				def     A_					(						self       :   str   ):
									snake_case_		     =							pipeline('''text-classification'''			,							framework='''tf'''   )

									snake_case_		     =							text_classifier('''This is great !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
									snake_case_		     =							text_classifier('''This is bad !'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
									snake_case_		     =							text_classifier('''Birds are a type of animal'''   )
									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': '''POSITIVE''', '''score''': 0.988}]   )


				def     A_					(						self       :   str			,							lowercase_       :   Optional[int]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   List[str]   ):
									snake_case_		     =							TextClassificationPipeline(model=lowercase_			,							tokenizer=lowercase_   )
									return text_classifier, ["HuggingFace is in", "This is another test"]


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									snake_case_		     =							text_classifier.model
									# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
									snake_case_		     =							'''HuggingFace is in'''
									snake_case_		     =							text_classifier(lowercase_   )

									self.assertEqual(nested_simplify(lowercase_   )			,							[{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}]   )
									self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )

									snake_case_		     =							['''HuggingFace is in ''', '''Paris is in France''']
									snake_case_		     =							text_classifier(lowercase_   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}, {'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}]			,							)
									self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )
									self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()   )

									# Forcing to get all results with `top_k=None`
									# This is NOT the legacy format
									snake_case_		     =							text_classifier(lowercase_			,							top_k=lowercase_   )
									snake_case_		     =							len(model.config.idalabel.values()   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[[{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}] * N, [{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}] * N]			,							)

									snake_case_		     =							{'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
									snake_case_		     =							text_classifier(lowercase_   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}			,							)
									self.assertTrue(outputs['''label'''] in model.config.idalabel.values()   )

									# This might be used a text pair, but tokenizer + pipe interaction
									# makes it hard to understand that it's not using the pair properly
									# https://github.com/huggingface/transformers/issues/17305
									# We disabled this usage instead as it was outputting wrong outputs.
									snake_case_		     =							[['''HuggingFace is in ''', '''Paris is in France''']]
									with self.assertRaises(lowercase_   ):
														text_classifier(lowercase_   )

									# This used to be valid for doing text pairs
									# We're keeping it working because of backward compatibility
									snake_case_		     =							text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]   )
									self.assertEqual(
									    nested_simplify(lowercase_   )			,							[{'''label''': ANY(lowercase_   ), '''score''': ANY(lowercase_   )}]			,							)
									self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()   )



"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



