


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.
_snake_case	:						List[Any]         =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Tuple	    =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase       :				str	    =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

							@require_torch
							def        __snake_case   (				self			:    Tuple							)      ->       List[Any]:
														__snake_case						:     Tuple									=  pipeline(
														    task="text-classification"       ,      model="hf-internal-testing/tiny-random-distilbert"       ,      framework="pt"							)

														__snake_case						:     int									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

														__snake_case						:     List[Any]									=  text_classifier("This is great !"       ,      top_k=2							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

														__snake_case						:     int									=  text_classifier(["This is great !", "This is bad"]       ,      top_k=2							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]       ,      )

														__snake_case						:     int									=  text_classifier("This is great !"       ,      top_k=1							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

														# Legacy behavior
														__snake_case						:     str									=  text_classifier("This is great !"       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

														__snake_case						:     str									=  text_classifier("This is great !"       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

														__snake_case						:     Optional[int]									=  text_classifier(["This is great !", "Something else"]       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]       ,      )

														__snake_case						:     str									=  text_classifier(["This is great !", "Something else"]       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]       ,      )

							@require_torch
							def        __snake_case   (				self			:    Optional[Any]							)      ->       str:
														import torch

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

														__snake_case						:     Any									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

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

														__snake_case						:     List[Any]									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

							@slow
							@require_torch
							def        __snake_case   (				self			:    Dict							)      ->       Dict:
														__snake_case						:     Optional[Any]									=  pipeline("text-classification"							)

														__snake_case						:     Optional[Any]									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 1.0}]							)
														__snake_case						:     Tuple									=  text_classifier("This is bad !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "NEGATIVE", "score": 1.0}]							)
														__snake_case						:     List[str]									=  text_classifier("Birds are a type of animal"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 0.9_88}]							)

							@slow
							@require_tf
							def        __snake_case   (				self			:    Tuple							)      ->       Dict:
														__snake_case						:     Tuple									=  pipeline("text-classification"       ,      framework="tf"							)

														__snake_case						:     int									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 1.0}]							)
														__snake_case						:     Dict									=  text_classifier("This is bad !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "NEGATIVE", "score": 1.0}]							)
														__snake_case						:     Dict									=  text_classifier("Birds are a type of animal"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 0.9_88}]							)

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    int							)      ->       Dict:
														__snake_case						:     List[str]									=  TextClassificationPipeline(model=lowerCamelCase       ,      tokenizer=lowerCamelCase							)
														return text_classifier, ["HuggingFace is in", "This is another test"]






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

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

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

														__snake_case						:     Any									=  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case						:     List[Any]									=  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.
														__snake_case						:     Dict									=  [["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
														__snake_case						:     Optional[int]									=  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()							)





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      a		:Optional[int]									=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      i -= len(UpperCAmelCase_		)
      a		:Tuple									=			i // 3
      a		:int									=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      a		:Union[str, Any]									=			(
          chars_incl
          + random(UpperCAmelCase_					,    quotient + remainder		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
      )
      a		:Dict									=			list(UpperCAmelCase_		)
      shuffle(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)

      # random is a generalised function for letters, characters and numbers
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      if len(UpperCAmelCase_		) < min_length:
            # Your Password must be at least 8 characters long
            return False

      a		:Dict									=			any(char in ascii_uppercase for char in password		)
      a		:Optional[int]									=			any(char in ascii_lowercase for char in password		)
      a		:Tuple									=			any(char in digits for char in password		)
      a		:Any									=			any(char in punctuation for char in password		)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:int									=			int(input('''Please indicate the max length of your password: '''		).strip()		)
      a		:Union[str, Any]									=			input(
          '''Please indicate the characters that must be in your password: '''		).strip()
      print('''Password generated:'''					,    password_generator(UpperCAmelCase_		)		)
      print(
          '''Alternative Password generated:'''					,    alternative_password_generator(UpperCAmelCase_					,    UpperCAmelCase_		)					,    )
      print('''[If you are thinking of using this passsword, You better save it.]'''		)


if __name__ == "__main__":
     main()





