

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 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			-1

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

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

         self.assertEqual(__lowerCAmelCase    ,						__lowerCAmelCase		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			-1

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

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

         self.assertEqual(__lowerCAmelCase    ,						__lowerCAmelCase		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			-1

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

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

         self.assertEqual(__lowerCAmelCase    ,						__lowerCAmelCase		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained("distilgpt2"		)
         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			-1

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

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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			-1

         lowerCamelCase__						=			ids_tensor((1, 5)    ,						vocab_size=model.config.vocab_size		).to(__lowerCAmelCase		)
         lowerCamelCase__						=			TextIteratorStreamer(__lowerCAmelCase    ,						timeout=0.0_01		)
         lowerCamelCase__						=			{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
         lowerCamelCase__						=			Thread(target=model.generate    ,						kwargs=__lowerCAmelCase		)
         thread.start()

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







"""simple docstring"""




# Algorithm for the pigeonhole sorting





def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			min(A__   )  # min() finds the minimum value
   lowerCamelCase__						=			max(A__   )  # max() finds the maximum value

   lowerCamelCase__						=			max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   lowerCamelCase__						=			[0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(A__	,    A__   ), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   lowerCamelCase__						=			0
   for count in range(A__   ):
      while holes[count] > 0:
         holes[count] -= 1
         lowerCamelCase__						=			count + min_val
         i += 1



def 	UpperCAmelCase__   (				)      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			[8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(A__   )
   print("Sorted order is:"	,    " ".join(A__   )   )


if __name__ == "__main__":
     main()




