'''simple docstring'''

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'''


      UpperCAmelCase_      :      int      							=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      UpperCAmelCase_      :      Optional[int]      							=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  TextaTextGenerationPipeline(model=lowerCamelCase_					,tokenizer=lowerCamelCase_							)
         return generator, ["Something to write", "Something else"]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  generator('''Something there'''							)
         self.assertEqual(lowerCamelCase_					,[{'''generated_text''': ANY(lowerCamelCase_							)}]							)
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''							)							)

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

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

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




      @require_torch
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  pipeline('''text2text-generation'''					,model='''patrickvonplaten/t5-tiny-random'''					,framework='''pt'''							)
         # do_sample=False necessary for reproducibility
         UpperCAmelCase__							:				Optional[int]   				=  generator('''Something there'''					,do_sample=lowerCamelCase_							)
         self.assertEqual(lowerCamelCase_					,[{'''generated_text''': ''''''}]							)

         UpperCAmelCase__							:				Union[str, Any]   				=  3
         UpperCAmelCase__							:				List[Any]   				=  generator(
             '''Something there'''					,num_return_sequences=lowerCamelCase_					,num_beams=lowerCamelCase_					,)
         UpperCAmelCase__							:				List[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(lowerCamelCase_					,lowerCamelCase_							)

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



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  pipeline('''text2text-generation'''					,model='''patrickvonplaten/t5-tiny-random'''					,framework='''tf'''							)
         # do_sample=False necessary for reproducibility
         UpperCAmelCase__							:				int   				=  generator('''Something there'''					,do_sample=lowerCamelCase_							)
         self.assertEqual(lowerCamelCase_					,[{'''generated_text''': ''''''}]							)





'''simple docstring'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




