


'''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.
__UpperCAmelCase       =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowercase__      :       List[Any]     						=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														lowercase__      :       Optional[Any]     						=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														lowercase__      :       Tuple     						=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt'''       )

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

														lowerCAmelCase__	       =     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}]       )

														lowerCAmelCase__	       =     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}],
														    ] ,   )

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

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

														lowerCAmelCase__	       =     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}]]       )

														lowerCAmelCase__	       =     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}],
														    ] ,   )

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														import torch

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

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







							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''tf'''       )

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







							@slow
							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     pipeline('''text-classification'''       )

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







							@slow
							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     pipeline('''text-classification''' ,   framework='''tf'''       )

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Tuple:
														lowerCAmelCase__	       =     TextClassificationPipeline(model=lowerCamelCase_ ,   tokenizer=lowerCamelCase_       )
														return text_classifier, ["HuggingFace is in", "This is another test"]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														lowerCAmelCase__	       =     '''HuggingFace is in'''
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )

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

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

														lowerCAmelCase__	       =     {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
														lowerCAmelCase__	       =     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.
														lowerCAmelCase__	       =     [['''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
														lowerCAmelCase__	       =     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()       )



'''simple docstring'''






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				CLIPTokenizer
							lowercase__      :       List[str]     						=				CLIPTokenizerFast
							lowercase__      :       Dict     						=				True
							lowercase__      :       Any     						=				{}
							lowercase__      :       Optional[int]     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														super().setUp()

														# fmt: off
														lowerCAmelCase__	       =     ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
														# fmt: on
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
														lowerCAmelCase__	       =     {'''unk_token''': '''<unk>'''}

														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''merges_file''']       )
														with open(self.vocab_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					fp.write(json.dumps(lowerCamelCase_       ) + '''\n'''       )
														with open(self.merges_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Any:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							int:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     '''lower newer'''
														return input_text, output_text







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     CLIPTokenizer(self.vocab_file ,   self.merges_file ,   **self.special_tokens_map       )
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
														lowerCAmelCase__	       =     tokenizer.tokenize(lowerCamelCase_       )
														self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokens + [tokenizer.unk_token]
														lowerCAmelCase__	       =     [10, 2, 16, 9, 3, 2, 16, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_       ) ,   lowerCamelCase_       )







							@require_ftfy
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     self.tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )
																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )

																												lowerCAmelCase__	       =     '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
																												lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																												# with Tilde) encoded in 2 different ways
																												lowerCAmelCase__	       =     '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
																												lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of space type
																												lowerCAmelCase__	       =     [
																												    '''\u0009''',  # (horizontal tab, '\t')
																												    '''\u000B''',  # (vertical tab)
																												    '''\u000C''',  # (form feed)
																												    '''\u0020''',  # (space, ' ')
																												    '''\u200E''',  # (left-to-right mark):w
																												    '''\u200F''',  # (right-to-left mark)
																												]
																												for unicode_seq in spaces_unicodes:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of line break type
																												lowerCAmelCase__	       =     [
																												    '''\u000A''',  # (line feed, '\n')
																												    '''\r\n''',  # (carriage return and line feed, '\r\n')
																												    '''\u000D''',  # (carriage return, '\r')
																												    '''\r''',  # (carriage return, '\r')
																												    '''\u000D''',  # (carriage return, '\r')
																												    '''\u2028''',  # (line separator)
																												    '''\u2029''',  # (paragraph separator)
																												    # "\u0085", # (next line)
																												]

																												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																												# space (and thus into an empty list).

																												for unicode_seq in line_break_unicodes:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																												lowerCAmelCase__	       =     F"""{text_of_1_token} {text_of_1_token}"""

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (len(lowerCamelCase_       ) + 1, len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     F""" {text}"""

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (1, 1 + len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (1 + len(lowerCamelCase_       ) + 1, 1 + len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# Test related to the breaking change introduced in transformers v4.17.0
														# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
														with self.assertRaises(lowerCamelCase_       ) as context:
																					self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''       )

														self.assertTrue(
														    context.exception.args[0].startswith(
														        '''The `backend_tokenizer` provided does not match the expected format.'''       )       )







							@require_ftfy
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														super().test_tokenization_python_rust_equals()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														# CLIP always lower cases letters
														pass