
'''simple docstring'''







import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


_lowercase :  Tuple    							=       get_logger(__name__)


_lowercase :  Union[str, Any]    							=       r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"






class  lowerCAmelCase__   :






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







class  lowerCAmelCase__   :






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											for processor in self:
																	lowercase_	:     Dict													=       inspect.signature(processor.__call__		).parameters
																	if len(__SCREAMING_SNAKE_CASE		) > 3:
																							if not all(arg in kwargs for arg in list(function_args.keys()		)[2:]		):
																													raise ValueError(
																													    F'''Make sure that all the required parameters: {list(function_args.keys()		)} for '''
																													    F'''{processor.__class__} are passed to the logits processor.'''		)
																							lowercase_	:     Dict													=       processor(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																	else:
																							lowercase_	:     Dict													=       processor(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or not (temperature > 0):
																	raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''		)

											lowercase_	:     int													=       temperature






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       scores / self.temperature
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = -float('''Inf'''		)				,							__SCREAMING_SNAKE_CASE = 1		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or (top_p < 0 or top_p > 1.0):
																	raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''		)
											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or (min_tokens_to_keep < 1):
																	raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''		)

											lowercase_	:     Optional[int]													=       top_p
											lowercase_	:     Optional[Any]													=       filter_value
											lowercase_	:     Optional[Any]													=       min_tokens_to_keep






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Optional[int]													=       lax.top_k(__SCREAMING_SNAKE_CASE				,							scores.shape[-1]		)

											lowercase_	:     List[str]													=       jnp.full_like(__SCREAMING_SNAKE_CASE				,							self.filter_value		)
											lowercase_	:     List[Any]													=       jax.nn.softmax(__SCREAMING_SNAKE_CASE				,							axis=-1		).cumsum(axis=-1		)
											lowercase_	:     Dict													=       cumulative_probs < self.top_p

											# include the token that is higher than top_p as well
											lowercase_	:     List[Any]													=       jnp.roll(__SCREAMING_SNAKE_CASE				,							1		)
											score_mask |= score_mask.at[:, 0].set(__SCREAMING_SNAKE_CASE		)

											# min tokens to keep
											lowercase_	:     List[str]													=       score_mask.at[:, : self.min_tokens_to_keep].set(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Any													=       jnp.where(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       jax.lax.sort_key_val(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)[-1]

											return next_scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = -float('''Inf'''		)				,							__SCREAMING_SNAKE_CASE = 1		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or top_k <= 0:
																	raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''		)

											lowercase_	:     str													=       max(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       filter_value






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Union[str, Any]													=       scores.shape
											lowercase_	:     int													=       jnp.full(batch_size * vocab_size				,							self.filter_value		)

											lowercase_	:     List[Any]													=       min(self.top_k				,							scores.shape[-1]		)  # Safety check
											lowercase_						, lowercase_	:     int													=       lax.top_k(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       jnp.broadcast_to((jnp.arange(__SCREAMING_SNAKE_CASE		) * vocab_size)[:, None]				,							(batch_size, topk)		).flatten()
											lowercase_	:     int													=       topk_scores.flatten()
											lowercase_	:     Tuple													=       topk_indices.flatten() + shift

											lowercase_	:     str													=       next_scores_flat.at[topk_indices_flat].set(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       next_scores_flat.reshape(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return next_scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       bos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       jnp.full(scores.shape				,							-float('''inf'''		)		)

											lowercase_	:     Union[str, Any]													=       1 - jnp.bool_(cur_len - 1		)

											lowercase_	:     Optional[int]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							new_scores.at[:, self.bos_token_id].set(0		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       max_length
											lowercase_	:     Union[str, Any]													=       eos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       jnp.full(scores.shape				,							-float('''inf'''		)		)

											lowercase_	:     List[str]													=       1 - jnp.bool_(cur_len - self.max_length + 1		)

											lowercase_	:     Union[str, Any]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							new_scores.at[:, self.eos_token_id].set(0		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or min_length < 0:
																	raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or eos_token_id < 0:
																	raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''		)

											lowercase_	:     str													=       min_length
											lowercase_	:     List[Any]													=       eos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       1 - jnp.clip(cur_len - self.min_length				,							0				,							1		)

											lowercase_	:     Dict													=       jnp.where(__SCREAMING_SNAKE_CASE				,							scores.at[:, self.eos_token_id].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       list(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       begin_index






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       1 - jnp.bool_(cur_len - self.begin_index		)

											lowercase_	:     Optional[int]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       list(__SCREAMING_SNAKE_CASE		)






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       scores.at[..., self.suppress_tokens].set(-float('''inf'''		)		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       dict(__SCREAMING_SNAKE_CASE		)
											# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
											# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
											# Indexes without forced tokens will have a negative value.
											lowercase_	:     Optional[int]													=       jnp.ones((max(force_token_map.keys()		) + 1)				,							dtype=jnp.intaa		) * -1
											for index, token in force_token_map.items():
																	if token is not None:
																							lowercase_	:     List[Any]													=       force_token_array.at[index].set(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       jnp.intaa(__SCREAMING_SNAKE_CASE		)






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											def _force_token(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Any													=       scores.shape[0]
																	lowercase_	:     str													=       self.force_token_array[generation_idx]

																	lowercase_	:     Union[str, Any]													=       jnp.ones_like(__SCREAMING_SNAKE_CASE				,							dtype=scores.dtype		) * -float('''inf'''		)
																	lowercase_	:     Optional[int]													=       jnp.zeros((batch_size, 1)				,							dtype=scores.dtype		)
																	lowercase_	:     int													=       lax.dynamic_update_slice(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							(0, current_token)		)
																	return new_scores

											lowercase_	:     Any													=       lax.cond(
											    cur_len >= self.force_token_array.shape[0]				,							lambda: scores				,							lambda: lax.cond(
											        self.force_token_array[cur_len] >= 0				,							lambda: _force_token(__SCREAMING_SNAKE_CASE		)				,							lambda: scores				,							)				,							)
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       generate_config.eos_token_id
											lowercase_	:     List[str]													=       generate_config.no_timestamps_token_id
											lowercase_	:     str													=       generate_config.no_timestamps_token_id + 1

											lowercase_	:     Any													=       decoder_input_length + 1

											if generate_config.is_multilingual:
																	# room for language token and task token
																	self.begin_index += 2
											if hasattr(__SCREAMING_SNAKE_CASE				,							'''max_initial_timestamp_index'''		):
																	lowercase_	:     List[Any]													=       generate_config.max_initial_timestamp_index
											else:
																	lowercase_	:     Dict													=       model_config.vocab_size
											if self.max_initial_timestamp_index is None:
																	lowercase_	:     Optional[Any]													=       model_config.vocab_size






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''		)		)

											def handle_pairs(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     int													=       jnp.where((cur_len - self.begin_index) >= 1				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Dict													=       jnp.where(
																	    input_ids_k[cur_len - 1] >= self.timestamp_begin				,							True and last_was_timestamp				,							__SCREAMING_SNAKE_CASE				,							)

																	lowercase_	:     Optional[int]													=       jnp.where((cur_len - self.begin_index) < 2				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     str													=       jnp.where(
																	    input_ids_k[cur_len - 2] >= self.timestamp_begin				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							)

																	return jnp.where(
																	    __SCREAMING_SNAKE_CASE				,							jnp.where(
																	        penultimate_was_timestamp > 0				,							scores_k.at[self.timestamp_begin :].set(-float('''inf'''		)		)				,							scores_k.at[: self.eos_token_id].set(-float('''inf'''		)		)				,							)				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Dict													=       jax.vmap(__SCREAMING_SNAKE_CASE		)(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       jnp.where(cur_len == self.begin_index				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       jnp.where(
											    self.max_initial_timestamp_index is not None				,							True and apply_max_initial_timestamp				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Union[str, Any]													=       self.timestamp_begin + self.max_initial_timestamp_index

											lowercase_	:     Optional[Any]													=       jnp.where(
											    __SCREAMING_SNAKE_CASE				,							scores.at[:, last_allowed + 1 :].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE				,							)

											# if sum of probability over timestamps is above any other token, sample timestamp
											lowercase_	:     List[Any]													=       jax.nn.log_softmax(__SCREAMING_SNAKE_CASE				,							axis=-1		)

											def handle_cumulative_probs(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Union[str, Any]													=       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,							axis=-1		)
																	lowercase_	:     List[str]													=       jnp.max(logprobs_k[: self.timestamp_begin]		)
																	return jnp.where(
																	    timestamp_logprob > max_text_token_logprob				,							scores_k.at[: self.timestamp_begin].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     int													=       jax.vmap(__SCREAMING_SNAKE_CASE		)(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											return scores








"""simple docstring"""
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  __lowerCAmelCase	(					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(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					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(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					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(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # 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(A       ,	A		)





     def  __lowerCAmelCase	(					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(A		)
         _UpperCAmelCase							:  Tuple											=    -1

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

         # 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(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					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(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

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


