"""simple docstring"""







import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    SqueezeBertForMaskedLM,
	    SqueezeBertForMultipleChoice,
	    SqueezeBertForQuestionAnswering,
	    SqueezeBertForSequenceClassification,
	    SqueezeBertForTokenClassification,
	    SqueezeBertModel,
	)

class 	UpperCamelCase    (					lowercase		):



				def __init__(self       :   Optional[Any]			,						_A       :   str			,						_A       :   Any=13			,						_A       :   Optional[Any]=7			,						_A       :   Union[str, Any]=True			,						_A       :   Dict=True			,						_A       :   Dict=False			,						_A       :   str=True			,						_A       :   List[Any]=99			,						_A       :   Optional[Any]=32			,						_A       :   int=5			,						_A       :   Optional[int]=4			,						_A       :   Union[str, Any]=64			,						_A       :   Any="gelu"			,						_A       :   Union[str, Any]=0.1			,						_A       :   Tuple=0.1			,						_A       :   List[Any]=5_12			,						_A       :   Any=16			,						_A       :   Dict=2			,						_A       :   List[str]=0.02			,						_A       :   List[Any]=3			,						_A       :   List[Any]=4			,						_A       :   Union[str, Any]=None			,						_A       :   List[Any]=2			,						_A       :   Optional[Any]=2			,						_A       :   Union[str, Any]=2			,						_A       :   Tuple=2			,						_A       :   List[str]=4			,						_A       :   List[Any]=1			,						)					->			List[str]:
					__snake_case    :							Any   	=				parent
					__snake_case    :							str   	=				batch_size
					__snake_case    :							str   	=				seq_length
					__snake_case    :							Any   	=				is_training
					__snake_case    :							Tuple   	=				use_input_mask
					__snake_case    :							Optional[int]   	=				use_token_type_ids
					__snake_case    :							Dict   	=				use_labels
					__snake_case    :							Any   	=				vocab_size
					__snake_case    :							int   	=				hidden_size
					__snake_case    :							Any   	=				num_hidden_layers
					__snake_case    :							Union[str, Any]   	=				num_attention_heads
					__snake_case    :							List[Any]   	=				intermediate_size
					__snake_case    :							Any   	=				hidden_act
					__snake_case    :							Optional[int]   	=				hidden_dropout_prob
					__snake_case    :							List[Any]   	=				attention_probs_dropout_prob
					__snake_case    :							Union[str, Any]   	=				max_position_embeddings
					__snake_case    :							Union[str, Any]   	=				type_vocab_size
					__snake_case    :							Any   	=				type_sequence_label_size
					__snake_case    :							Optional[int]   	=				initializer_range
					__snake_case    :							Optional[int]   	=				num_labels
					__snake_case    :							Tuple   	=				num_choices
					__snake_case    :							Dict   	=				scope
					__snake_case    :							Dict   	=				q_groups
					__snake_case    :							Union[str, Any]   	=				k_groups
					__snake_case    :							int   	=				v_groups
					__snake_case    :							Any   	=				post_attention_groups
					__snake_case    :							Tuple   	=				intermediate_groups
					__snake_case    :							int   	=				output_groups



				def       _lowercase  (self       :   Tuple)					->			Any:
					__snake_case    :							Union[str, Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							Optional[int]   	=				None
					if self.use_input_mask:
						__snake_case    :							Any   	=				random_attention_mask([self.batch_size, self.seq_length])

					__snake_case    :							List[str]   	=				None
					__snake_case    :							Optional[Any]   	=				None
					__snake_case    :							Optional[int]   	=				None
					if self.use_labels:
						__snake_case    :							List[Any]   	=				ids_tensor([self.batch_size]			,						self.type_sequence_label_size)
						__snake_case    :							Union[str, Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels)
						__snake_case    :							Optional[int]   	=				ids_tensor([self.batch_size]			,						self.num_choices)

					__snake_case    :							List[str]   	=				self.get_config()

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



				def       _lowercase  (self       :   Union[str, Any])					->			List[str]:
					return SqueezeBertConfig(
					    embedding_size=self.hidden_size			,						vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						attention_probs_dropout_prob=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						q_groups=self.q_groups			,						k_groups=self.k_groups			,						v_groups=self.v_groups			,						post_attention_groups=self.post_attention_groups			,						intermediate_groups=self.intermediate_groups			,						output_groups=self.output_groups			,						)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Dict			,						_A       :   Dict			,						_A       :   int			,						_A       :   Tuple			,						_A       :   Any			,						_A       :   int)					->			Optional[int]:
					__snake_case    :							Optional[int]   	=				SqueezeBertModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Union[str, Any]   	=				model(_A			,						_A)
					__snake_case    :							Union[str, Any]   	=				model(_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   Dict			,						_A       :   Tuple			,						_A       :   int			,						_A       :   Optional[int]			,						_A       :   int			,						_A       :   List[str]			,						_A       :   Tuple)					->			Dict:
					__snake_case    :							Optional[Any]   	=				SqueezeBertForMaskedLM(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							str   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   List[Any]			,						_A       :   Any			,						_A       :   str			,						_A       :   int			,						_A       :   List[Any]			,						_A       :   int)					->			Tuple:
					__snake_case    :							Optional[Any]   	=				SqueezeBertForQuestionAnswering(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Optional[int]   	=				model(
					    _A			,						attention_mask=_A			,						start_positions=_A			,						end_positions=_A)
					self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length))
					self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length))



				def       _lowercase  (self       :   Tuple			,						_A       :   Union[str, Any]			,						_A       :   int			,						_A       :   int			,						_A       :   Optional[int]			,						_A       :   int			,						_A       :   Any)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				self.num_labels
					__snake_case    :							Any   	=				SqueezeBertForSequenceClassification(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Tuple   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))



				def       _lowercase  (self       :   List[str]			,						_A       :   Any			,						_A       :   Optional[int]			,						_A       :   int			,						_A       :   Optional[Any]			,						_A       :   List[Any]			,						_A       :   Optional[int])					->			Union[str, Any]:
					__snake_case    :							List[str]   	=				self.num_labels
					__snake_case    :							Any   	=				SqueezeBertForTokenClassification(config=_A)
					model.to(_A)
					model.eval()

					__snake_case    :							Dict   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels))



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   str			,						_A       :   Dict			,						_A       :   Union[str, Any]			,						_A       :   Tuple			,						_A       :   List[str]			,						_A       :   Any)					->			Dict:
					__snake_case    :							Optional[int]   	=				self.num_choices
					__snake_case    :							Optional[int]   	=				SqueezeBertForMultipleChoice(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							str   	=				input_ids.unsqueeze(1).expand(-1			,						self.num_choices			,						-1).contiguous()
					__snake_case    :							Tuple   	=				input_mask.unsqueeze(1).expand(-1			,						self.num_choices			,						-1).contiguous()
					__snake_case    :							Dict   	=				model(
					    _A			,						attention_mask=_A			,						labels=_A			,						)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices))






				def       _lowercase  (self       :   Union[str, Any])					->			str:
					__snake_case    :							Optional[Any]   	=				self.prepare_config_and_inputs()
					((__snake_case)	,       (__snake_case)	,       (__snake_case)	,       (__snake_case)	,       (__snake_case)	,       (__snake_case))    :							Union[str, Any]   	=				config_and_inputs
					__snake_case    :							str   	=				{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Dict					=	(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				UpperCAmelCase       :   List[Any]					=	(
				    {
				        """feature-extraction""": SqueezeBertModel,
				        """fill-mask""": SqueezeBertForMaskedLM,
				        """question-answering""": SqueezeBertForQuestionAnswering,
				        """text-classification""": SqueezeBertForSequenceClassification,
				        """token-classification""": SqueezeBertForTokenClassification,
				        """zero-shot""": SqueezeBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   List[str]					=	True
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   int)					->			Dict:
					__snake_case    :							Optional[int]   	=				SqueezeBertModelTester(self)
					__snake_case    :							Dict   	=				ConfigTester(self			,						config_class=_A			,						dim=37)



				def       _lowercase  (self       :   Dict)					->			List[str]:
					self.config_tester.run_common_tests()



				def       _lowercase  (self       :   Any)					->			str:
					__snake_case    :							List[Any]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_model(*_A)



				def       _lowercase  (self       :   Any)					->			Union[str, Any]:
					__snake_case    :							Optional[int]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_masked_lm(*_A)



				def       _lowercase  (self       :   int)					->			Dict:
					__snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_question_answering(*_A)



				def       _lowercase  (self       :   Tuple)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_A)



				def       _lowercase  (self       :   int)					->			List[str]:
					__snake_case    :							Optional[int]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_token_classification(*_A)



				def       _lowercase  (self       :   Any)					->			List[Any]:
					__snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_A)






				@slow
				def       _lowercase  (self       :   Optional[Any])					->			List[str]:
					for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							str   	=				SqueezeBertModel.from_pretrained(_A)
						self.assertIsNotNone(_A)







@require_sentencepiece
@require_tokenizers
@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				@slow
				def       _lowercase  (self       :   Optional[Any])					->			Optional[Any]:
					__snake_case    :							str   	=				SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')

					__snake_case    :							List[Any]   	=				torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
					__snake_case    :							Dict   	=				model(_A)[0]
					__snake_case    :							Optional[int]   	=				torch.Size((1, 3))
					self.assertEqual(output.shape			,						_A)
					__snake_case    :							Tuple   	=				torch.tensor([[0.6_401, -0.0_349, -0.6_041]])
					self.assertTrue(torch.allclose(_A			,						_A			,						atol=1E-4))






"""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		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

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

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

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

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			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
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

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






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				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):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text





