






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers import AutoModelForCausalLM







@require_torch
class 			UpperCAmelCase_						(					unittest.TestCase ):



      """simple docstring"""
      def     UpperCAmelCase						(				self )				->      List[str]:
          UpperCamelCase       :Any								=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
          UpperCamelCase       :Dict								=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[str]								=   -1

          UpperCamelCase       :List[str]								=   ids_tensor((1, 5)    ,				vocab_size=model.config.vocab_size ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[str]								=   model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=10    ,				do_sample=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   tokenizer.decode(greedy_ids[0] )

          with CaptureStdout() as cs:
              UpperCamelCase       :str								=   TextStreamer(SCREAMING_SNAKE_CASE_ )
              model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=10    ,				do_sample=SCREAMING_SNAKE_CASE_    ,				streamer=SCREAMING_SNAKE_CASE_ )
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          UpperCamelCase       :List[str]								=   cs.out[:-1]

          self.assertEqual(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self )				->      Dict:
          UpperCamelCase       :List[str]								=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
          UpperCamelCase       :List[Any]								=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   -1

          UpperCamelCase       :Union[str, Any]								=   ids_tensor((1, 5)    ,				vocab_size=model.config.vocab_size ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=10    ,				do_sample=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :str								=   tokenizer.decode(greedy_ids[0] )

          UpperCamelCase       :Union[str, Any]								=   TextIteratorStreamer(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Any								=   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          UpperCamelCase       :Any								=   Thread(target=model.generate    ,				kwargs=SCREAMING_SNAKE_CASE_ )
          thread.start()
          UpperCamelCase       :Any								=   ''''''
          for new_text in streamer:
              streamer_text += new_text

          self.assertEqual(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Any								=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
          UpperCamelCase       :str								=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Any								=   -1

          UpperCamelCase       :Dict								=   ids_tensor((1, 5)    ,				vocab_size=model.config.vocab_size ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=10    ,				do_sample=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   greedy_ids[:, input_ids.shape[1] :]
          UpperCamelCase       :List[str]								=   tokenizer.decode(new_greedy_ids[0] )

          with CaptureStdout() as cs:
              UpperCamelCase       :Any								=   TextStreamer(SCREAMING_SNAKE_CASE_    ,				skip_prompt=SCREAMING_SNAKE_CASE_ )
              model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=10    ,				do_sample=SCREAMING_SNAKE_CASE_    ,				streamer=SCREAMING_SNAKE_CASE_ )
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          UpperCamelCase       :Union[str, Any]								=   cs.out[:-1]

          self.assertEqual(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
          # with actual models -- the dummy models' tokenizers are not aligned with their models, and
          # `skip_special_tokens=True` has no effect on them
          UpperCamelCase       :int								=   AutoTokenizer.from_pretrained('''distilgpt2''' )
          UpperCamelCase       :Union[str, Any]								=   AutoModelForCausalLM.from_pretrained('''distilgpt2''' ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Tuple								=   -1

          UpperCamelCase       :int								=   torch.ones((1, 5)    ,				device=SCREAMING_SNAKE_CASE_ ).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              UpperCamelCase       :Optional[Any]								=   TextStreamer(SCREAMING_SNAKE_CASE_    ,				skip_special_tokens=SCREAMING_SNAKE_CASE_ )
              model.generate(SCREAMING_SNAKE_CASE_    ,				max_new_tokens=1    ,				do_sample=SCREAMING_SNAKE_CASE_    ,				streamer=SCREAMING_SNAKE_CASE_ )

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          UpperCamelCase       :Tuple								=   cs.out[:-1]  # Remove the final "\n"
          UpperCamelCase       :int								=   tokenizer(SCREAMING_SNAKE_CASE_    ,				return_tensors='''pt''' )
          self.assertEqual(streamer_text_tokenized.input_ids.shape    ,				(1, 1) )




      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :Any								=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' )
          UpperCamelCase       :Any								=   AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2''' ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   -1

          UpperCamelCase       :str								=   ids_tensor((1, 5)    ,				vocab_size=model.config.vocab_size ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   TextIteratorStreamer(SCREAMING_SNAKE_CASE_    ,				timeout=0.001 )
          UpperCamelCase       :Union[str, Any]								=   {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          UpperCamelCase       :Optional[Any]								=   Thread(target=model.generate    ,				kwargs=SCREAMING_SNAKE_CASE_ )
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
              UpperCamelCase       :Optional[Any]								=   ''''''
              for new_text in streamer:
                  streamer_text += new_text










"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


if __name__ == "__main__":
  print(f"{solution() = }")


