






"""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 						A_							(      unittest.TestCase  ):







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

										__magic_name__            =     ids_tensor((1, 5)						,    vocab_size=model.config.vocab_size	).to(lowerCamelCase__	)
										__magic_name__            =     model.generate(lowerCamelCase__						,    max_new_tokens=1_0						,    do_sample=lowerCamelCase__	)
										__magic_name__            =     tokenizer.decode(greedy_ids[0]	)

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

										self.assertEqual(lowerCamelCase__						,    lowerCamelCase__	)







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

										__magic_name__            =     ids_tensor((1, 5)						,    vocab_size=model.config.vocab_size	).to(lowerCamelCase__	)
										__magic_name__            =     model.generate(lowerCamelCase__						,    max_new_tokens=1_0						,    do_sample=lowerCamelCase__	)
										__magic_name__            =     tokenizer.decode(greedy_ids[0]	)

										__magic_name__            =     TextIteratorStreamer(lowerCamelCase__	)
										__magic_name__            =     {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
										__magic_name__            =     Thread(target=model.generate						,    kwargs=lowerCamelCase__	)
										thread.start()
										__magic_name__            =     ''''''
										for new_text in streamer:
																streamer_text += new_text

										self.assertEqual(lowerCamelCase__						,    lowerCamelCase__	)







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

										__magic_name__            =     ids_tensor((1, 5)						,    vocab_size=model.config.vocab_size	).to(lowerCamelCase__	)
										__magic_name__            =     model.generate(lowerCamelCase__						,    max_new_tokens=1_0						,    do_sample=lowerCamelCase__	)
										__magic_name__            =     greedy_ids[:, input_ids.shape[1] :]
										__magic_name__            =     tokenizer.decode(new_greedy_ids[0]	)

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

										self.assertEqual(lowerCamelCase__						,    lowerCamelCase__	)







				def     _snake_case					(							self							:						Dict	)				->					Any:
										__magic_name__            =     AutoTokenizer.from_pretrained("distilgpt2"	)
										__magic_name__            =     AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(lowerCamelCase__	)
										__magic_name__            =     -1

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







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

										__magic_name__            =     ids_tensor((1, 5)						,    vocab_size=model.config.vocab_size	).to(lowerCamelCase__	)
										__magic_name__            =     TextIteratorStreamer(lowerCamelCase__						,    timeout=0.001	)
										__magic_name__            =     {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
										__magic_name__            =     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__	):
																__magic_name__            =     ''''''
																for new_text in streamer:
																						streamer_text += new_text











"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       ['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowercase    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



