




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
       import torch




@is_pipeline_test
class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _snake_case												=						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    List[Any]:
         snake_case				:    int           =	TextaTextGenerationPipeline(model=A    ,				tokenizer=A     )
         return generator, ["Something to write", "Something else"]

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    Dict:
         snake_case				:    Any           =	generator('''Something there'''     )
         self.assertEqual(A    ,				[{'''generated_text''': ANY(A     )}]     )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''     )     )

         snake_case				:    Optional[int]           =	generator(['''This is great !''', '''Something else''']    ,				num_return_sequences=2    ,				do_sample=A     )
         self.assertEqual(
             A    ,				[
                 [{'''generated_text''': ANY(A     )}, {'''generated_text''': ANY(A     )}],
                 [{'''generated_text''': ANY(A     )}, {'''generated_text''': ANY(A     )}],
             ]    ,				)

         snake_case				:    List[Any]           =	generator(
             ['''This is great !''', '''Something else''']    ,				num_return_sequences=2    ,				batch_size=2    ,				do_sample=A     )
         self.assertEqual(
             A    ,				[
                 [{'''generated_text''': ANY(A     )}, {'''generated_text''': ANY(A     )}],
                 [{'''generated_text''': ANY(A     )}, {'''generated_text''': ANY(A     )}],
             ]    ,				)

         with self.assertRaises(A     ):
            generator(4     )

      @require_torch
      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    Dict           =	pipeline('''text2text-generation'''    ,				model='''patrickvonplaten/t5-tiny-random'''    ,				framework='''pt'''     )
         # do_sample=False necessary for reproducibility
         snake_case				:    int           =	generator('''Something there'''    ,				do_sample=A     )
         self.assertEqual(A    ,				[{'''generated_text''': ''''''}]     )

         snake_case				:    str           =	3
         snake_case				:    int           =	generator(
             '''Something there'''    ,				num_return_sequences=A    ,				num_beams=A    ,				)
         snake_case				:    Any           =	[
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(A    ,				A     )

         snake_case				:    List[str]           =	generator('''This is a test'''    ,				do_sample=A    ,				num_return_sequences=2    ,				return_tensors=A     )
         self.assertEqual(
             A    ,				[
                 {'''generated_token_ids''': ANY(torch.Tensor     )},
                 {'''generated_token_ids''': ANY(torch.Tensor     )},
             ]    ,				)
         snake_case				:    Any           =	generator.model.config.eos_token_id
         snake_case				:    Any           =	"""<pad>"""
         snake_case				:    Optional[int]           =	generator(
             ['''This is a test''', '''This is a second test''']    ,				do_sample=A    ,				num_return_sequences=2    ,				batch_size=2    ,				return_tensors=A    ,				)
         self.assertEqual(
             A    ,				[
                 [
                     {'''generated_token_ids''': ANY(torch.Tensor     )},
                     {'''generated_token_ids''': ANY(torch.Tensor     )},
                 ],
                 [
                     {'''generated_token_ids''': ANY(torch.Tensor     )},
                     {'''generated_token_ids''': ANY(torch.Tensor     )},
                 ],
             ]    ,				)

      @require_tf
      def      UpperCAmelCase				(				self     )		->    int:
         snake_case				:    int           =	pipeline('''text2text-generation'''    ,				model='''patrickvonplaten/t5-tiny-random'''    ,				framework='''tf'''     )
         # do_sample=False necessary for reproducibility
         snake_case				:    List[Any]           =	generator('''Something there'''    ,				do_sample=A     )
         self.assertEqual(A    ,				[{'''generated_text''': ''''''}]     )











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





