






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     __snake_case       ( self  : Any						):
											lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
											lowerCAmelCase__         =    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    -1

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

											with CaptureStdout() as cs:
															lowerCAmelCase__         =    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
											lowerCAmelCase__         =    cs.out[:-1]

											self.assertEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)




							def     __snake_case       ( 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(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    -1

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

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

											self.assertEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)




							def     __snake_case       ( self  : Dict						):
											lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
											lowerCAmelCase__         =    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    -1

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

											with CaptureStdout() as cs:
															lowerCAmelCase__         =    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
											lowerCAmelCase__         =    cs.out[:-1]

											self.assertEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)




							def     __snake_case       ( self  : List[str]						):
											# 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
											lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''distilgpt2'''						)
											lowerCAmelCase__         =    AutoModelForCausalLM.from_pretrained('''distilgpt2'''						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    -1

											lowerCAmelCase__         =    torch.ones((1, 5)			,				device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
															lowerCAmelCase__         =    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
											lowerCAmelCase__         =    cs.out[:-1]  # Remove the final "\n"
											lowerCAmelCase__         =    tokenizer(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''						)
											self.assertEqual(streamer_text_tokenized.input_ids.shape			,				(1, 1)						)




							def     __snake_case       ( self  : List[str]						):
											lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
											lowerCAmelCase__         =    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    -1

											lowerCAmelCase__         =    ids_tensor((1, 5)			,				vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    TextIteratorStreamer(SCREAMING_SNAKE_CASE_			,				timeout=0.001						)
											lowerCAmelCase__         =    {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
											lowerCAmelCase__         =    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_						):
															lowerCAmelCase__         =    ''''''
															for new_text in streamer:
																			streamer_text += new_text







"""simple docstring"""


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType




@dataclass
class  __lowerCamelCase  :
				a__:      List[str]
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='Translation'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def __call__(				self   ):
							return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )





				def        UpperCAmelCase__						(				self   ):
							from .features import Value

							return {k: Value('''string'''   ) for k in sorted(self.languages   )}




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[List]          =    None
				a__:      Optional[int]          =    None
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='TranslationVariableLanguages'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  sorted(set(self.languages   )   ) if self.languages else None
							lowerCamelCase_					=  len(self.languages   ) if self.languages else None




				def __call__(				self   ):
							return pa.struct({'''language''': pa.list_(pa.string()   ), '''translation''': pa.list_(pa.string()   )}   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  set(self.languages   )
							if self.languages and set(UpperCAmelCase   ) - lang_set:
										raise ValueError(
										    f"Some languages in example ({', '.join(sorted(set(UpperCAmelCase   ) - lang_set   )   )}) are not in valid set ({', '.join(UpperCAmelCase   )})."   )

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							lowerCamelCase_					=  []
							for lang, text in translation_dict.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													translation_tuples.append((lang, text)   )
										else:
													translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
							lowerCamelCase_		, lowerCamelCase_					=  zip(*sorted(UpperCAmelCase   )   )

							return {"language": languages, "translation": translations}





				def        UpperCAmelCase__						(				self   ):
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('''string'''   )   ),
							    "translation": Sequence(Value('''string'''   )   ),
							}



