'''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 			__A (					unittest.TestCase      ):
    a__			:		Tuple			  =    MODEL_FOR_CAUSAL_LM_MAPPING
    a__			:		str			  =    TF_MODEL_FOR_CAUSAL_LM_MAPPING



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

         UpperCAmelCase_			       =  text_generator(["This is a test", "This is a second test"]					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 [
                     {
                         "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@@"
                         )
                     }
                 ],
             ]  ,     )

         UpperCAmelCase_			       =  text_generator("This is a test"  ,     do_sample=_SCREAMING_SNAKE_CASE  ,     num_return_sequences=2  ,     return_tensors=_SCREAMING_SNAKE_CASE					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
                 {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
             ]  ,     )
         UpperCAmelCase_			       =  text_generator.model.config.eos_token_id
         UpperCAmelCase_			       =  "<pad>"
         UpperCAmelCase_			       =  text_generator(
             ["This is a test", "This is a second test"]  ,     do_sample=_SCREAMING_SNAKE_CASE  ,     num_return_sequences=2  ,     batch_size=2  ,     return_tensors=_SCREAMING_SNAKE_CASE  ,     )
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 [
                     {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
                     {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
                 ],
                 [
                     {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
                     {"generated_token_ids": ANY(_SCREAMING_SNAKE_CASE					)},
                 ],
             ]  ,     )



    @require_tf
    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  pipeline(task="text-generation"  ,     model="sshleifer/tiny-ctrl"  ,     framework="tf"					)

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

         UpperCAmelCase_			       =  text_generator(["This is a test", "This is a second test"]  ,     do_sample=_SCREAMING_SNAKE_CASE					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 [
                     {
                         "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    _lowercase		(self       :       Tuple  ,     __a       :       Any  ,     __a       :       List[str]  ,     __a       :       Dict					):
         UpperCAmelCase_			       =  TextGenerationPipeline(model=_SCREAMING_SNAKE_CASE  ,     tokenizer=_SCREAMING_SNAKE_CASE					)
         return text_generator, ["This is a test", "Another test"]



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  "Hello I believe in"
         UpperCAmelCase_			       =  pipeline("text-generation"  ,     model="hf-internal-testing/tiny-random-gpt2"					)
         UpperCAmelCase_			       =  text_generator(_SCREAMING_SNAKE_CASE					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}]  ,     )

         UpperCAmelCase_			       =  text_generator(_SCREAMING_SNAKE_CASE  ,     stop_sequence=" fe"					)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": "Hello I believe in fe"}]					)



    def    _lowercase		(self       :       List[str]  ,     __a       :       Optional[int]  ,     __a       :       Dict					):
         UpperCAmelCase_			       =  text_generator.model
         UpperCAmelCase_			       =  text_generator.tokenizer

         UpperCAmelCase_			       =  text_generator("This is a test"					)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}]					)
         self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"					)					)

         UpperCAmelCase_			       =  text_generator("This is a test"  ,     return_full_text=_SCREAMING_SNAKE_CASE					)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}]					)
         self.assertNotIn("This is a test"  ,     outputs[0]["generated_text"]					)

         UpperCAmelCase_			       =  pipeline(task="text-generation"  ,     model=_SCREAMING_SNAKE_CASE  ,     tokenizer=_SCREAMING_SNAKE_CASE  ,     return_full_text=_SCREAMING_SNAKE_CASE					)
         UpperCAmelCase_			       =  text_generator("This is a test"					)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}]					)
         self.assertNotIn("This is a test"  ,     outputs[0]["generated_text"]					)

         UpperCAmelCase_			       =  text_generator("This is a test"  ,     return_full_text=_SCREAMING_SNAKE_CASE					)
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}]					)
         self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"					)					)

         UpperCAmelCase_			       =  text_generator(["This is great !", "Something else"]  ,     num_return_sequences=2  ,     do_sample=_SCREAMING_SNAKE_CASE					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}, {"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}],
                 [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}, {"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}],
             ]  ,     )

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

         with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              UpperCAmelCase_			       =  text_generator("test"  ,     return_full_text=_SCREAMING_SNAKE_CASE  ,     return_text=_SCREAMING_SNAKE_CASE					)
         with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              UpperCAmelCase_			       =  text_generator("test"  ,     return_full_text=_SCREAMING_SNAKE_CASE  ,     return_tensors=_SCREAMING_SNAKE_CASE					)
         with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              UpperCAmelCase_			       =  text_generator("test"  ,     return_text=_SCREAMING_SNAKE_CASE  ,     return_tensors=_SCREAMING_SNAKE_CASE					)

         # 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__
         ):
              UpperCAmelCase_			       =  text_generator(""					)
              self.assertEqual(_SCREAMING_SNAKE_CASE  ,     [{"generated_text": ANY(_SCREAMING_SNAKE_CASE					)}]					)
         else:
              with self.assertRaises((ValueError, AssertionError)					):
                   UpperCAmelCase_			       =  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.
         UpperCAmelCase_			       =  ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
         if (
             tokenizer.model_max_length < 10000
             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					)

              UpperCAmelCase_			       =  text_generator("This is a test" * 500  ,     handle_long_generation="hole"  ,     max_new_tokens=20					)
              # Hole strategy cannot work
              with self.assertRaises(_SCREAMING_SNAKE_CASE					):
                   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    _lowercase		(self       :       Any					):
         import torch

         # Classic `model_kwargs`
         UpperCAmelCase_			       =  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					)
         UpperCAmelCase_			       =  pipe("This is a test"					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 {
                     "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.)
         UpperCAmelCase_			       =  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					)
         UpperCAmelCase_			       =  pipe("This is a test"					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 {
                     "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
         UpperCAmelCase_			       =  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					)
         UpperCAmelCase_			       =  pipe("This is a test"					)
         self.assertEqual(
             _SCREAMING_SNAKE_CASE  ,     [
                 {
                     "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    _lowercase		(self       :       str					):
         import torch

         UpperCAmelCase_			       =  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    _lowercase		(self       :       List[str]					):
         import torch

         UpperCAmelCase_			       =  pipeline(model="hf-internal-testing/tiny-random-bloom"  ,     device_map="auto"  ,     torch_dtype=torch.floataa					)
         pipe("This is a test"  ,     do_sample=_SCREAMING_SNAKE_CASE  ,     top_p=0.5					)






    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  "Hello world"
         UpperCAmelCase_			       =  pipeline("text-generation"  ,     model="hf-internal-testing/tiny-random-gpt2"					)
         if text_generator.model.framework == "tf":
              UpperCAmelCase_			       =  logging.get_logger("transformers.generation.tf_utils"					)
         else:
              UpperCAmelCase_			       =  logging.get_logger("transformers.generation.utils"					)
         UpperCAmelCase_			       =  "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(_SCREAMING_SNAKE_CASE					) as cl:
              UpperCAmelCase_			       =  text_generator(_SCREAMING_SNAKE_CASE  ,     max_length=10  ,     max_new_tokens=1					)
         self.assertIn(_SCREAMING_SNAKE_CASE  ,     cl.out					)

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

         with CaptureLogger(_SCREAMING_SNAKE_CASE					) as cl:
              UpperCAmelCase_			       =  text_generator(_SCREAMING_SNAKE_CASE  ,     max_length=10					)
         self.assertNotIn(_SCREAMING_SNAKE_CASE  ,     cl.out					)










'''simple docstring'''





from __future__ import annotations

from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     None:
          _snake_case				= num_of_nodes
          _snake_case				= []
          _snake_case				= {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:
          self.m_edges.append([u_node, v_node, weight]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     int:

          if self.m_component[u_node] == u_node:
               return u_node
          return self.find_component(self.m_component[u_node]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if self.m_component[u_node] != u_node:
               for k in self.m_component:
                    _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if component_size[u_node] <= component_size[v_node]:
               _snake_case				= v_node
               component_size[v_node] += component_size[u_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )

          elif component_size[u_node] >= component_size[v_node]:
               _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )
               component_size[u_node] += component_size[v_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= []
          _snake_case				= 0

          _snake_case				= [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
               self.m_component.update({node: node}       )
               component_size.append(1       )

          _snake_case				= self.m_num_of_nodes

          while num_of_components > 1:
               for edge in self.m_edges:
                    _snake_case				,   _snake_case				,   _snake_case				= edge

                    _snake_case				= self.m_component[u]
                    _snake_case				= self.m_component[v]

                    if u_component != v_component:

                         for component in (u_component, v_component):
                              if (
                                  minimum_weight_edge[component] == -1
                                  or minimum_weight_edge[component][2] > w
                              ):
                                   _snake_case				= [u, v, w]

               for edge in minimum_weight_edge:
                    if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         _snake_case				,   _snake_case				,   _snake_case				= edge

                         _snake_case				= self.m_component[u]
                         _snake_case				= self.m_component[v]

                         if u_component != v_component:
                              mst_weight += w
                              self.union(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                              print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                              num_of_components -= 1

               _snake_case				= [-1] * self.m_num_of_nodes
          print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""       )







def       __a      (  )		->					None:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()


