


"""simple docstring"""







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		a__        =  '''pt'''
elif is_tf_available():
		a__        =  '''tf'''
else:
		a__        =  '''jax'''




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Tuple					      =    PerceiverTokenizer
	snake_case__       :						List[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			int:
						super().setUp()
						__SCREAMING_SNAKE_CASE		     =       PerceiverTokenizer()
						tokenizer.save_pretrained(self.tmpdirname     )




	@cached_property
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Optional[Any]:
						return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    **UpperCAmelCase__    :       Dict     ) ->			PerceiverTokenizer:
						return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Optional[Any]=False    ,    UpperCAmelCase__    :       Optional[int]=2_0    ,    UpperCAmelCase__    :       List[Any]=5     ) ->			Tuple[str, list]:
						# XXX The default common tokenizer tests assume that every ID is decodable on its own.
						# This assumption is invalid for Perceiver because single bytes might not be
						# valid utf-8 (byte 128 for instance).
						# Here we're overriding the smallest possible method to provide
						# a clean sequence without making the same assumption.

						__SCREAMING_SNAKE_CASE		     =       []
						for i in range(len(UpperCAmelCase__     )     ):
											try:
																__SCREAMING_SNAKE_CASE		     =       tokenizer.decode([i]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											except UnicodeDecodeError:
																pass
											toks.append((i, tok)     )

						__SCREAMING_SNAKE_CASE		     =       list(filter(lambda UpperCAmelCase__     : re.match(R"^[ a-zA-Z]+$"    ,    t[1]     )    ,    UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       list(filter(lambda UpperCAmelCase__     : [t[0]] == tokenizer.encode(t[1]    ,    add_special_tokens=UpperCAmelCase__     )    ,    UpperCAmelCase__     )     )
						if max_length is not None and len(UpperCAmelCase__     ) > max_length:
											__SCREAMING_SNAKE_CASE		     =       toks[:max_length]
						if min_length is not None and len(UpperCAmelCase__     ) < min_length and len(UpperCAmelCase__     ) > 0:
											while len(UpperCAmelCase__     ) < min_length:
																__SCREAMING_SNAKE_CASE		     =       toks + toks
        # toks_str = [t[1] for t in toks]
						__SCREAMING_SNAKE_CASE		     =       [t[0] for t in toks]

						# Ensure consistency
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
						if " " not in output_txt and len(UpperCAmelCase__     ) > 1:
											__SCREAMING_SNAKE_CASE		     =       (
											    tokenizer.decode([toks_ids[0]]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											    + " "
											    + tokenizer.decode(toks_ids[1:]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											)
						if with_prefix_space:
											__SCREAMING_SNAKE_CASE		     =       " " + output_txt
						__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
						return output_txt, output_ids




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       "Unicode €."
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
						self.assertEqual(encoded["input_ids"]    ,    UpperCAmelCase__     )

						# decoding
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__     )
						self.assertEqual(UpperCAmelCase__    ,    "[CLS]Unicode €.[SEP]"     )

						__SCREAMING_SNAKE_CASE		     =       tokenizer("e è é ê ë"     )
						__SCREAMING_SNAKE_CASE		     =       [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
						self.assertEqual(encoded["input_ids"]    ,    UpperCAmelCase__     )
						# decoding
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__     )
						self.assertEqual(UpperCAmelCase__    ,    "[CLS]e è é ê ë[SEP]"     )

						# encode/decode, but with `encode` instead of `__call__`
						self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"     )     )    ,    "[CLS]e è é ê ë[SEP]"     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						# fmt: off
						__SCREAMING_SNAKE_CASE		     =       [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
						# fmt: on
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )

						if FRAMEWORK != "jax":
											__SCREAMING_SNAKE_CASE		     =       list(batch.input_ids.numpy()[0]     )
						else:
											__SCREAMING_SNAKE_CASE		     =       list(batch.input_ids.tolist()[0]     )

						self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )

						self.assertEqual((2, 3_8)    ,    batch.input_ids.shape     )
						self.assertEqual((2, 3_8)    ,    batch.attention_mask.shape     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn("input_ids"    ,    UpperCAmelCase__     )
						self.assertIn("attention_mask"    ,    UpperCAmelCase__     )
						self.assertNotIn("decoder_input_ids"    ,    UpperCAmelCase__     )
						self.assertNotIn("decoder_attention_mask"    ,    UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       [
						    "Summary of the text.",
						    "Another summary.",
						]
						__SCREAMING_SNAKE_CASE		     =       tokenizer(
						    text_target=UpperCAmelCase__    ,    max_length=3_2    ,    padding="max_length"    ,    truncation=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						self.assertEqual(3_2    ,    targets["input_ids"].shape[1]     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						# safety check on max_len default value so we are sure the test works
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																self.assertNotEqual(tokenizer.model_max_length    ,    4_2     )

        # Now let's start the test
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																# Isolate this from the other tests because we save additional tokens/etc
																__SCREAMING_SNAKE_CASE		     =       tempfile.mkdtemp()

																__SCREAMING_SNAKE_CASE		     =       " He is very happy, UNwant\u00E9d,running"
																__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																tokenizer.save_pretrained(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       after_tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )

																shutil.rmtree(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers(model_max_length=4_2     )
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																# Isolate this from the other tests because we save additional tokens/etc
																__SCREAMING_SNAKE_CASE		     =       tempfile.mkdtemp()

																__SCREAMING_SNAKE_CASE		     =       " He is very happy, UNwant\u00E9d,running"
																tokenizer.add_tokens(["bim", "bambam"]     )
																__SCREAMING_SNAKE_CASE		     =       tokenizer.additional_special_tokens
																additional_special_tokens.append("new_additional_special_token"     )
																tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}     )
																__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																tokenizer.save_pretrained(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       after_tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )
																self.assertIn("new_additional_special_token"    ,    after_tokenizer.additional_special_tokens     )
																self.assertEqual(after_tokenizer.model_max_length    ,    4_2     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__    ,    model_max_length=4_3     )
																self.assertEqual(tokenizer.model_max_length    ,    4_3     )

																shutil.rmtree(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       []
						if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

						if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(UpperCAmelCase__     )

																with open(os.path.join(UpperCAmelCase__    ,    "special_tokens_map.json"     )    ,    encoding="utf-8"     ) as json_file:
																					__SCREAMING_SNAKE_CASE		     =       json.load(UpperCAmelCase__     )

																with open(os.path.join(UpperCAmelCase__    ,    "tokenizer_config.json"     )    ,    encoding="utf-8"     ) as json_file:
																					__SCREAMING_SNAKE_CASE		     =       json.load(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       [F"""<extra_id_{i}>""" for i in range(1_2_5     )]

																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [
																    "an_additional_special_token"
																]
																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [
																    "an_additional_special_token"
																]

																with open(os.path.join(UpperCAmelCase__    ,    "special_tokens_map.json"     )    ,    "w"    ,    encoding="utf-8"     ) as outfile:
																					json.dump(UpperCAmelCase__    ,    UpperCAmelCase__     )
																with open(os.path.join(UpperCAmelCase__    ,    "tokenizer_config.json"     )    ,    "w"    ,    encoding="utf-8"     ) as outfile:
																					json.dump(UpperCAmelCase__    ,    UpperCAmelCase__     )

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																__SCREAMING_SNAKE_CASE		     =       tokenizer_class.from_pretrained(
																    UpperCAmelCase__    ,    )
																self.assertIn(
																    "an_additional_special_token"    ,    tokenizer_without_change_in_init.additional_special_tokens     )
																self.assertEqual(
																    ["an_additional_special_token"]    ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]     )     )    ,    )

																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"    ,    lstrip=UpperCAmelCase__     )]
																__SCREAMING_SNAKE_CASE		     =       tokenizer_class.from_pretrained(
																    UpperCAmelCase__    ,    additional_special_tokens=UpperCAmelCase__    ,    )

																self.assertIn("a_new_additional_special_token"    ,    tokenizer.additional_special_tokens     )
																self.assertEqual(
																    ["a_new_additional_special_token"]    ,    tokenizer.convert_ids_to_tokens(
																        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]     )     )    ,    )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						self.assertEqual(tokenizer.decode([1_7_8]     )    ,    "�"     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						pass




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						pass




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Tuple:
						pass




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			int:
						pass




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
						# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
						# strings and special added tokens as tokens
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers(fast=UpperCAmelCase__    ,    do_lower_case=UpperCAmelCase__     )
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																__SCREAMING_SNAKE_CASE		     =       ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																__SCREAMING_SNAKE_CASE		     =       tokenizer.convert_tokens_to_string(UpperCAmelCase__     )

																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )




"""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__       :							Dict        =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						str					      =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	snake_case__       :						Tuple					      =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		snake_case__       :						List[Any]					      =    {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__       :						Dict					      =    {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}




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

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

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

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

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

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

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

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

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




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						import torch

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

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




	@require_tf
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="tf"     )

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




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"     )

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




	@slow
	@require_tf
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"    ,    framework="tf"     )

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




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Tuple     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       TextClassificationPipeline(model=UpperCAmelCase__    ,    tokenizer=UpperCAmelCase__     )
						return text_classifier, ["HuggingFace is in", "This is another test"]




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       text_classifier.model
						# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
						__SCREAMING_SNAKE_CASE		     =       "HuggingFace is in"
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )

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

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

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

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