
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 						lowercase__  (						unittest.TestCase  ):
	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
						SCREAMING_SNAKE_CASE__													= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= -1

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

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

						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
						SCREAMING_SNAKE_CASE__													= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= -1

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

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

						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
						SCREAMING_SNAKE_CASE__													= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= -1

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

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

						self.assertEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						# 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
						SCREAMING_SNAKE_CASE__													= AutoTokenizer.from_pretrained('distilgpt2'							)
						SCREAMING_SNAKE_CASE__													= AutoModelForCausalLM.from_pretrained('distilgpt2'							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= -1

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

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





	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
						SCREAMING_SNAKE_CASE__													= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							).to(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= -1

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

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

