





'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):
     lowerCAmelCase		      =	MODEL_FOR_CAUSAL_LM_MAPPING
     lowerCAmelCase		      =	TF_MODEL_FOR_CAUSAL_LM_MAPPING


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	pipeline(task='text-generation'						,     model='sshleifer/tiny-ctrl'						,     framework='pt')
          # Using `do_sample=False` to force deterministic output
          __A						:			str								=	text_generator('This is a test'						,     do_sample=_UpperCAmelCase)
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {
                      'generated_text': (
                          'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                          ' oscope. FiliFili@@'
                      )
                  }
              ]						,     )

          __A						:			int								=	text_generator(['This is a test', 'This is a second test'])
          self.assertEqual(
              _UpperCAmelCase						,     [
                  [
                      {
                          'generated_text': (
                              'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                              ' oscope. FiliFili@@'
                          )
                      }
                  ],
                  [
                      {
                          'generated_text': (
                              'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                              ' oscope. oscope. FiliFili@@'
                          )
                      }
                  ],
              ]						,     )

          __A						:			Any								=	text_generator('This is a test'						,     do_sample=_UpperCAmelCase						,     num_return_sequences=2						,     return_tensors=_UpperCAmelCase)
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {'generated_token_ids': ANY(_UpperCAmelCase)},
                  {'generated_token_ids': ANY(_UpperCAmelCase)},
              ]						,     )
          __A						:			List[str]								=	text_generator.model.config.eos_token_id
          __A						:			Optional[int]								=	'<pad>'
          __A						:			Tuple								=	text_generator(
              ['This is a test', 'This is a second test']						,     do_sample=_UpperCAmelCase						,     num_return_sequences=2						,     batch_size=2						,     return_tensors=_UpperCAmelCase						,     )
          self.assertEqual(
              _UpperCAmelCase						,     [
                  [
                      {'generated_token_ids': ANY(_UpperCAmelCase)},
                      {'generated_token_ids': ANY(_UpperCAmelCase)},
                  ],
                  [
                      {'generated_token_ids': ANY(_UpperCAmelCase)},
                      {'generated_token_ids': ANY(_UpperCAmelCase)},
                  ],
              ]						,     )


     @require_tf
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	pipeline(task='text-generation'						,     model='sshleifer/tiny-ctrl'						,     framework='tf')

          # Using `do_sample=False` to force deterministic output
          __A						:			Optional[int]								=	text_generator('This is a test'						,     do_sample=_UpperCAmelCase)
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {
                      'generated_text': (
                          'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                          ' please,'
                      )
                  }
              ]						,     )

          __A						:			str								=	text_generator(['This is a test', 'This is a second test']						,     do_sample=_UpperCAmelCase)
          self.assertEqual(
              _UpperCAmelCase						,     [
                  [
                      {
                          'generated_text': (
                              'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                              ' please,'
                          )
                      }
                  ],
                  [
                      {
                          'generated_text': (
                              'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                              ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                          )
                      }
                  ],
              ]						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	TextGenerationPipeline(model=_UpperCAmelCase						,     tokenizer=_UpperCAmelCase)
          return text_generator, ["This is a test", "Another test"]


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	'Hello I believe in'
          __A						:			str								=	pipeline('text-generation'						,     model='hf-internal-testing/tiny-random-gpt2')
          __A						:			Any								=	text_generator(_UpperCAmelCase)
          self.assertEqual(
              _UpperCAmelCase						,     [{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]						,     )

          __A						:			List[str]								=	text_generator(_UpperCAmelCase						,     stop_sequence=' fe')
          self.assertEqual(_UpperCAmelCase						,     [{'generated_text': 'Hello I believe in fe'}])


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[int]								=	text_generator.model
          __A						:			Union[str, Any]								=	text_generator.tokenizer

          __A						:			Union[str, Any]								=	text_generator('This is a test')
          self.assertEqual(_UpperCAmelCase						,     [{'generated_text': ANY(_UpperCAmelCase)}])
          self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'))

          __A						:			Optional[Any]								=	text_generator('This is a test'						,     return_full_text=_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     [{'generated_text': ANY(_UpperCAmelCase)}])
          self.assertNotIn('This is a test'						,     outputs[0]['generated_text'])

          __A						:			Any								=	pipeline(task='text-generation'						,     model=_UpperCAmelCase						,     tokenizer=_UpperCAmelCase						,     return_full_text=_UpperCAmelCase)
          __A						:			Optional[int]								=	text_generator('This is a test')
          self.assertEqual(_UpperCAmelCase						,     [{'generated_text': ANY(_UpperCAmelCase)}])
          self.assertNotIn('This is a test'						,     outputs[0]['generated_text'])

          __A						:			List[str]								=	text_generator('This is a test'						,     return_full_text=_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     [{'generated_text': ANY(_UpperCAmelCase)}])
          self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'))

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

          if text_generator.tokenizer.pad_token is not None:
               __A						:			int								=	text_generator(
                   ['This is great !', 'Something else']						,     num_return_sequences=2						,     batch_size=2						,     do_sample=_UpperCAmelCase)
               self.assertEqual(
                   _UpperCAmelCase						,     [
                       [{'generated_text': ANY(_UpperCAmelCase)}, {'generated_text': ANY(_UpperCAmelCase)}],
                       [{'generated_text': ANY(_UpperCAmelCase)}, {'generated_text': ANY(_UpperCAmelCase)}],
                   ]						,     )

          with self.assertRaises(_UpperCAmelCase):
               __A						:			Dict								=	text_generator('test'						,     return_full_text=_UpperCAmelCase						,     return_text=_UpperCAmelCase)
          with self.assertRaises(_UpperCAmelCase):
               __A						:			int								=	text_generator('test'						,     return_full_text=_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)
          with self.assertRaises(_UpperCAmelCase):
               __A						:			Optional[int]								=	text_generator('test'						,     return_text=_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)

          # Empty prompt is slighly special
          # it requires BOS token to exist.
          # Special case for Pegasus which will always append EOS so will
          # work even without BOS.
          if (
              text_generator.tokenizer.bos_token_id is not None
              or "Pegasus" in tokenizer.__class__.__name__
              or "Git" in model.__class__.__name__
          ):
               __A						:			Dict								=	text_generator('')
               self.assertEqual(_UpperCAmelCase						,     [{'generated_text': ANY(_UpperCAmelCase)}])
          else:
               with self.assertRaises((ValueError, AssertionError)):
                    __A						:			Optional[Any]								=	text_generator('')

          if text_generator.framework == "tf":
               # TF generation does not support max_new_tokens, and it's impossible
               # to control long generation with only max_length without
               # fancy calculation, dismissing tests for now.
               return
          # We don't care about infinite range models.
          # They already work.
          # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
          __A						:			Optional[Any]								=	['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
          if (
              tokenizer.model_max_length < 1_0000
              and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
          ):
               # Handling of large generations
               with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
                    text_generator('This is a test' * 500						,     max_new_tokens=20)

               __A						:			Tuple								=	text_generator('This is a test' * 500						,     handle_long_generation='hole'						,     max_new_tokens=20)
               # Hole strategy cannot work
               with self.assertRaises(_UpperCAmelCase):
                    text_generator(
                        'This is a test' * 500						,     handle_long_generation='hole'						,     max_new_tokens=tokenizer.model_max_length + 10						,     )


     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          import torch

          # Classic `model_kwargs`
          __A						:			Union[str, Any]								=	pipeline(
              model='hf-internal-testing/tiny-random-bloom'						,     model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}						,     )
          self.assertEqual(pipe.model.device						,     torch.device(0))
          self.assertEqual(pipe.model.lm_head.weight.dtype						,     torch.bfloataa)
          __A						:			Union[str, Any]								=	pipe('This is a test')
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {
                      'generated_text': (
                          'This is a test test test test test test test test test test test test test test test test'
                          ' test'
                      )
                  }
              ]						,     )

          # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
          __A						:			Tuple								=	pipeline(model='hf-internal-testing/tiny-random-bloom'						,     device_map='auto'						,     torch_dtype=torch.bfloataa)
          self.assertEqual(pipe.model.device						,     torch.device(0))
          self.assertEqual(pipe.model.lm_head.weight.dtype						,     torch.bfloataa)
          __A						:			Optional[int]								=	pipe('This is a test')
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {
                      'generated_text': (
                          'This is a test test test test test test test test test test test test test test test test'
                          ' test'
                      )
                  }
              ]						,     )

          # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
          __A						:			Optional[Any]								=	pipeline(model='hf-internal-testing/tiny-random-bloom'						,     device_map='auto')
          self.assertEqual(pipe.model.device						,     torch.device(0))
          self.assertEqual(pipe.model.lm_head.weight.dtype						,     torch.floataa)
          __A						:			Union[str, Any]								=	pipe('This is a test')
          self.assertEqual(
              _UpperCAmelCase						,     [
                  {
                      'generated_text': (
                          'This is a test test test test test test test test test test test test test test test test'
                          ' test'
                      )
                  }
              ]						,     )


     @require_torch
     @require_torch_gpu
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          import torch

          __A						:			Optional[Any]								=	pipeline(model='hf-internal-testing/tiny-random-bloom'						,     device=0						,     torch_dtype=torch.floataa)
          pipe('This is a test')


     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          import torch

          __A						:			List[Any]								=	pipeline(model='hf-internal-testing/tiny-random-bloom'						,     device_map='auto'						,     torch_dtype=torch.floataa)
          pipe('This is a test'						,     do_sample=_UpperCAmelCase						,     top_p=0.5)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	'Hello world'
          __A						:			Any								=	pipeline('text-generation'						,     model='hf-internal-testing/tiny-random-gpt2')
          if text_generator.model.framework == "tf":
               __A						:			Union[str, Any]								=	logging.get_logger('transformers.generation.tf_utils')
          else:
               __A						:			Dict								=	logging.get_logger('transformers.generation.utils')
          __A						:			Optional[Any]								=	'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

          # Both are set by the user -> log warning
          with CaptureLogger(_UpperCAmelCase) as cl:
               __A						:			List[Any]								=	text_generator(_UpperCAmelCase						,     max_length=10						,     max_new_tokens=1)
          self.assertIn(_UpperCAmelCase						,     cl.out)

          # The user only sets one -> no warning
          with CaptureLogger(_UpperCAmelCase) as cl:
               __A						:			Any								=	text_generator(_UpperCAmelCase						,     max_new_tokens=1)
          self.assertNotIn(_UpperCAmelCase						,     cl.out)

          with CaptureLogger(_UpperCAmelCase) as cl:
               __A						:			str								=	text_generator(_UpperCAmelCase						,     max_length=10)
          self.assertNotIn(_UpperCAmelCase						,     cl.out)






'''simple docstring'''




from collections.abc import Iterable
from typing import Any


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			str								=	value
          __A						:			Node | None								=	None  # Added in order to delete a node easier
          __A						:			Node | None								=	None
          __A						:			Node | None								=	None


     def __repr__(						self):

          '''simple docstring'''






          from pprint import pformat

          if self.left is None and self.right is None:
               return str(self.value)
          return pformat({F'{self.value}': (self.left, self.right)}						,     indent=1)




class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			int								=	root


     def __str__(						self):

          '''simple docstring'''






          return str(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if new_children is not None:  # reset its kids
               __A						:			Optional[int]								=	node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(_UpperCAmelCase):  # If it is the right children
                    __A						:			int								=	new_children
               else:
                    __A						:			Union[str, Any]								=	new_children
          else:
               __A						:			Tuple								=	new_children


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node.parent and node.parent.right:
               return node == node.parent.right
          return False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.root is None


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	Node(_UpperCAmelCase)  # create a new Node
          if self.empty():  # if Tree is empty
               __A						:			Union[str, Any]								=	new_node  # set its root
          else:  # Tree is not empty
               __A						:			Dict								=	self.root  # from root
               if parent_node is None:
                    return
               while True:  # While we don't get to a leaf
                    if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                              __A						:			Tuple								=	new_node  # We insert the new node in a leaf
                              break
                         else:
                              __A						:			str								=	parent_node.left
                    else:
                         if parent_node.right is None:
                              __A						:			List[str]								=	new_node
                              break
                         else:
                              __A						:			int								=	parent_node.right
               __A						:			int								=	parent_node


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase):

          '''simple docstring'''






          for value in values:
               self.__insert(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if self.empty():
               raise IndexError('Warning: Tree is empty! please use another.')
          else:
               __A						:			Any								=	self.root
               # use lazy evaluation here to avoid NoneType Attribute error
               while node is not None and node.value is not value:
                    __A						:			List[Any]								=	node.left if value < node.value else node.right
               return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               if self.root is None:
                    return None
               __A						:			str								=	self.root

          if not self.empty():
               while node.right is not None:
                    __A						:			Dict								=	node.right
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               __A						:			Optional[Any]								=	self.root
          if self.root is None:
               return None
          if not self.empty():
               __A						:			Optional[int]								=	self.root
               while node.left is not None:
                    __A						:			Tuple								=	node.left
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.search(_UpperCAmelCase)  # Look for the node with that label
          if node is not None:
               if node.left is None and node.right is None:  # If it has no children
                    self.__reassign_nodes(_UpperCAmelCase						,     _UpperCAmelCase)
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.right)
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.left)
               else:
                    __A						:			str								=	self.get_max(
                        node.left)  # Gets the max value of the left branch
                    self.remove(tmp_node.value)  # type: ignore
                    __A						:			List[Any]								=	(
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left)
               yield from self.preorder_traverse(node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=None):

          '''simple docstring'''






          if traversal_function is None:
               return self.preorder_traverse(self.root)
          else:
               return traversal_function(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if node:
               self.inorder(_UpperCAmelCase						,     node.left)
               arr.append(node.value)
               self.inorder(_UpperCAmelCase						,     node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			list[int]								=	[]
          self.inorder(_UpperCAmelCase						,     _UpperCAmelCase)  # append all values to list using inorder traversal
          return arr[k - 1]






def 	_lowerCAmelCase      (  __snake_case  :			Node | None      )					->					list[Node]:
     __A						:			Tuple								=	[]
     if curr_node is not None:
          __A						:			Optional[Any]								=	postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
     return node_list






def 	_lowerCAmelCase      (  )					->					None:
     __A						:			Optional[int]								=	(8, 3, 6, 1, 10, 14, 13, 4, 7)
     __A						:			str								=	BinarySearchTree()
     for i in testlist:
          t.insert(__snake_case      )

     # Prints all the elements of the list in order traversal
     print(__snake_case      )

     if t.search(6      ) is not None:
          print('The value 6 exists'      )
     else:
          print('The value 6 doesn\'t exist'      )

     if t.search(-1      ) is not None:
          print('The value -1 exists'      )
     else:
          print('The value -1 doesn\'t exist'      )

     if not t.empty():
          print('Max Value: '			,							t.get_max().value      )  # type: ignore
          print('Min Value: '			,							t.get_min().value      )  # type: ignore

     for i in testlist:
          t.remove(__snake_case      )
          print(__snake_case      )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)