



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.
SCREAMING_SNAKE_CASE     				=						{"LayoutLMv2Config", "LayoutLMv3Config"}



@is_pipeline_test
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[int]									=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					lowerCAmelCase_				:    Optional[int]									=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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



					@require_torch
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline(
							    task="text-classification"    ,	model="hf-internal-testing/tiny-random-distilbert"    ,	framework="pt"     )

							UpperCAmelCase_						=				text_classifier("This is great !"     )
							self.assertEqual(nested_simplify(lowerCAmelCase     )    ,	[{"label": "LABEL_0", "score": 0.504}]     )

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

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

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

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

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

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

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



					@require_torch
					def    A__		(			self     ):
							import torch

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

							UpperCAmelCase_						=				text_classifier("This is great !"     )
							self.assertEqual(nested_simplify(lowerCAmelCase     )    ,	[{"label": "LABEL_0", "score": 0.504}]     )



					@require_tf
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline(
							    task="text-classification"    ,	model="hf-internal-testing/tiny-random-distilbert"    ,	framework="tf"     )

							UpperCAmelCase_						=				text_classifier("This is great !"     )
							self.assertEqual(nested_simplify(lowerCAmelCase     )    ,	[{"label": "LABEL_0", "score": 0.504}]     )



					@slow
					@require_torch
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline("text-classification"     )

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



					@slow
					@require_tf
					def    A__		(			self     ):
							UpperCAmelCase_						=				pipeline("text-classification"    ,	framework="tf"     )

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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				TextClassificationPipeline(model=lowerCAmelCase    ,	tokenizer=lowerCAmelCase     )
							return text_classifier, ["HuggingFace is in", "This is another test"]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				text_classifier.model
							# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
							UpperCAmelCase_						=				"HuggingFace is in"
							UpperCAmelCase_						=				text_classifier(lowerCAmelCase     )

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

							UpperCAmelCase_						=				["HuggingFace is in ", "Paris is in France"]
							UpperCAmelCase_						=				text_classifier(lowerCAmelCase     )
							self.assertEqual(
							    nested_simplify(lowerCAmelCase     )    ,	[{"label": ANY(lowerCAmelCase     ), "score": ANY(lowerCAmelCase     )}, {"label": ANY(lowerCAmelCase     ), "score": ANY(lowerCAmelCase     )}]    ,	)
							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
							UpperCAmelCase_						=				text_classifier(lowerCAmelCase    ,	top_k=lowerCAmelCase     )
							UpperCAmelCase_						=				len(model.config.idalabel.values()     )
							self.assertEqual(
							    nested_simplify(lowerCAmelCase     )    ,	[[{"label": ANY(lowerCAmelCase     ), "score": ANY(lowerCAmelCase     )}] * N, [{"label": ANY(lowerCAmelCase     ), "score": ANY(lowerCAmelCase     )}] * N]    ,	)

							UpperCAmelCase_						=				{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
							UpperCAmelCase_						=				text_classifier(lowerCAmelCase     )
							self.assertEqual(
							    nested_simplify(lowerCAmelCase     )    ,	{"label": ANY(lowerCAmelCase     ), "score": ANY(lowerCAmelCase     )}    ,	)
							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.
							UpperCAmelCase_						=				[["HuggingFace is in ", "Paris is in France"]]
							with self.assertRaises(lowerCAmelCase     ):
									text_classifier(lowerCAmelCase     )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

