





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  lowerCAmelCase							(       unittest.TestCase ):







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
     lowercase__			       =  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowercase )
     lowercase__			       =  -1

     lowercase__			       =  ids_tensor((1, 5)   ,							vocab_size=model.config.vocab_size ).to(_lowercase )
     lowercase__			       =  model.generate(_lowercase   ,							max_new_tokens=10   ,							do_sample=_lowercase )
     lowercase__			       =  tokenizer.decode(greedy_ids[0] )

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

     self.assertEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
     lowercase__			       =  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowercase )
     lowercase__			       =  -1

     lowercase__			       =  ids_tensor((1, 5)   ,							vocab_size=model.config.vocab_size ).to(_lowercase )
     lowercase__			       =  model.generate(_lowercase   ,							max_new_tokens=10   ,							do_sample=_lowercase )
     lowercase__			       =  tokenizer.decode(greedy_ids[0] )

     lowercase__			       =  TextIteratorStreamer(_lowercase )
     lowercase__			       =  {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     lowercase__			       =  Thread(target=model.generate   ,							kwargs=_lowercase )
     thread.start()
     lowercase__			       =  ""
     for new_text in streamer:
        streamer_text += new_text

     self.assertEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
     lowercase__			       =  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowercase )
     lowercase__			       =  -1

     lowercase__			       =  ids_tensor((1, 5)   ,							vocab_size=model.config.vocab_size ).to(_lowercase )
     lowercase__			       =  model.generate(_lowercase   ,							max_new_tokens=10   ,							do_sample=_lowercase )
     lowercase__			       =  greedy_ids[:, input_ids.shape[1] :]
     lowercase__			       =  tokenizer.decode(new_greedy_ids[0] )

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

     self.assertEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  AutoTokenizer.from_pretrained("distilgpt2" )
     lowercase__			       =  AutoModelForCausalLM.from_pretrained("distilgpt2" ).to(_lowercase )
     lowercase__			       =  -1

     lowercase__			       =  torch.ones((1, 5)   ,							device=_lowercase ).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
        lowercase__			       =  TextStreamer(_lowercase   ,							skip_special_tokens=_lowercase )
        model.generate(_lowercase   ,							max_new_tokens=1   ,							do_sample=_lowercase   ,							streamer=_lowercase )

     # 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
     lowercase__			       =  cs.out[:-1]  # Remove the final "\n"
     lowercase__			       =  tokenizer(_lowercase   ,							return_tensors="pt" )
     self.assertEqual(streamer_text_tokenized.input_ids.shape   ,							(1, 1) )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
     lowercase__			       =  AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowercase )
     lowercase__			       =  -1

     lowercase__			       =  ids_tensor((1, 5)   ,							vocab_size=model.config.vocab_size ).to(_lowercase )
     lowercase__			       =  TextIteratorStreamer(_lowercase   ,							timeout=0.001 )
     lowercase__			       =  {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     lowercase__			       =  Thread(target=model.generate   ,							kwargs=_lowercase )
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowercase ):
        lowercase__			       =  ""
        for new_text in streamer:
           streamer_text += new_text













from __future__ import annotations

from collections import deque





class  lowerCAmelCase							:







  def __init__(      self  :List[Any]   ,							_lowercase  :list[str] ):






     '''simple docstring'''





     lowercase__			       =  []
     self.adlist.append(
         {"value": "", "next_states": [], "fail_state": 0, "output": []} )

     for keyword in keywords:
        self.add_keyword(_lowercase )
     self.set_fail_transitions()







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :int   ,							_lowercase  :str ):






     '''simple docstring'''





     for state in self.adlist[current_state]["next_states"]:
        if char == self.adlist[state]["value"]:
           return state
     return None







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  0
     for character in keyword:
        lowercase__			       =  self.find_next_state(_lowercase   ,							_lowercase )
        if next_state is None:
           self.adlist.append(
               {
                   "value": character,
                   "next_states": [],
                   "fail_state": 0,
                   "output": [],
               } )
           self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
           lowercase__			       =  len(self.adlist ) - 1
        else:
           lowercase__			       =  next_state
     self.adlist[current_state]["output"].append(_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  deque()
     for node in self.adlist[0]["next_states"]:
        q.append(_lowercase )
        lowercase__			       =  0
     while q:
        lowercase__			       =  q.popleft()
        for child in self.adlist[r]["next_states"]:
           q.append(_lowercase )
           lowercase__			       =  self.adlist[r]["fail_state"]
           while (
               self.find_next_state(_lowercase   ,							self.adlist[child]["value"] ) is None
               and state != 0
           ):
              lowercase__			       =  self.adlist[state]["fail_state"]
           lowercase__			       =  self.find_next_state(
               _lowercase   ,							self.adlist[child]["value"] )
           if self.adlist[child]["fail_state"] is None:
              lowercase__			       =  0
           lowercase__			       =  (
               self.adlist[child]["output"]
               + self.adlist[self.adlist[child]["fail_state"]]["output"]
           )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  {}  # returns a dict with keywords and list of its occurrences
     lowercase__			       =  0
     for i in range(len(_lowercase ) ):
        while (
            self.find_next_state(_lowercase   ,							string[i] ) is None
            and current_state != 0
        ):
           lowercase__			       =  self.adlist[current_state]["fail_state"]
        lowercase__			       =  self.find_next_state(_lowercase   ,							string[i] )
        if next_state is None:
           lowercase__			       =  0
        else:
           lowercase__			       =  next_state
           for key in self.adlist[current_state]["output"]:
              if key not in result:
                 lowercase__			       =  []
              result[key].append(i - len(_lowercase ) + 1 )
     return result


if __name__ == "__main__":
  import doctest

  doctest.testmod()






