






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     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=13     ,						_UpperCamelCase=7     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						_UpperCamelCase=99     ,						_UpperCamelCase=32     ,						_UpperCamelCase=5     ,						_UpperCamelCase=4     ,						_UpperCamelCase=64     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=16     ,						_UpperCamelCase=2     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=3     ,						_UpperCamelCase=4     ,						_UpperCamelCase=None     ,						_UpperCamelCase=2     ,						_UpperCamelCase=2     ,						_UpperCamelCase=2     ,						_UpperCamelCase=2     ,						_UpperCamelCase=4     ,						_UpperCamelCase=1     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						parent
												lowerCAmelCase__    						=						batch_size
												lowerCAmelCase__    						=						seq_length
												lowerCAmelCase__    						=						is_training
												lowerCAmelCase__    						=						use_input_mask
												lowerCAmelCase__    						=						use_token_type_ids
												lowerCAmelCase__    						=						use_labels
												lowerCAmelCase__    						=						vocab_size
												lowerCAmelCase__    						=						hidden_size
												lowerCAmelCase__    						=						num_hidden_layers
												lowerCAmelCase__    						=						num_attention_heads
												lowerCAmelCase__    						=						intermediate_size
												lowerCAmelCase__    						=						hidden_act
												lowerCAmelCase__    						=						hidden_dropout_prob
												lowerCAmelCase__    						=						attention_probs_dropout_prob
												lowerCAmelCase__    						=						max_position_embeddings
												lowerCAmelCase__    						=						type_vocab_size
												lowerCAmelCase__    						=						type_sequence_label_size
												lowerCAmelCase__    						=						initializer_range
												lowerCAmelCase__    						=						num_labels
												lowerCAmelCase__    						=						num_choices
												lowerCAmelCase__    						=						scope
												lowerCAmelCase__    						=						q_groups
												lowerCAmelCase__    						=						k_groups
												lowerCAmelCase__    						=						v_groups
												lowerCAmelCase__    						=						post_attention_groups
												lowerCAmelCase__    						=						intermediate_groups
												lowerCAmelCase__    						=						output_groups






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size   )

												lowerCAmelCase__    						=						None
												if self.use_input_mask:
																		lowerCAmelCase__    						=						random_attention_mask([self.batch_size, self.seq_length]   )

												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						None
												if self.use_labels:
																		lowerCAmelCase__    						=						ids_tensor([self.batch_size]     ,						self.type_sequence_label_size   )
																		lowerCAmelCase__    						=						ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels   )
																		lowerCAmelCase__    						=						ids_tensor([self.batch_size]     ,						self.num_choices   )

												lowerCAmelCase__    						=						self.get_config()

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						SqueezeBertModel(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						model(_UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						SqueezeBertForMaskedLM(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						SqueezeBertForQuestionAnswering(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(
												    _UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						start_positions=_UpperCamelCase     ,						end_positions=_UpperCamelCase   )
												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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_labels
												lowerCAmelCase__    						=						SqueezeBertForSequenceClassification(_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_labels
												lowerCAmelCase__    						=						SqueezeBertForTokenClassification(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()

												lowerCAmelCase__    						=						model(_UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.num_choices
												lowerCAmelCase__    						=						SqueezeBertForMultipleChoice(config=_UpperCamelCase   )
												model.to(_UpperCamelCase   )
												model.eval()
												lowerCAmelCase__    						=						input_ids.unsqueeze(1   ).expand(-1     ,						self.num_choices     ,						-1   ).contiguous()
												lowerCAmelCase__    						=						input_mask.unsqueeze(1   ).expand(-1     ,						self.num_choices     ,						-1   ).contiguous()
												lowerCAmelCase__    						=						model(
												    _UpperCamelCase     ,						attention_mask=_UpperCamelCase     ,						labels=_UpperCamelCase     ,						)
												self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.prepare_config_and_inputs()
												((lowerCAmelCase__)						,       (lowerCAmelCase__)						,       (lowerCAmelCase__)						,       (lowerCAmelCase__)						,       (lowerCAmelCase__)						,       (lowerCAmelCase__))    						=						config_and_inputs
												lowerCAmelCase__    						=						{'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict




@require_torch
class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					__lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       str   =					(
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						_SCREAMING_SNAKE_CASE	:       List[str]   =					(
						    {
						        '''feature-extraction''': SqueezeBertModel,
						        '''fill-mask''': SqueezeBertForMaskedLM,
						        '''question-answering''': SqueezeBertForQuestionAnswering,
						        '''text-classification''': SqueezeBertForSequenceClassification,
						        '''token-classification''': SqueezeBertForTokenClassification,
						        '''zero-shot''': SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE	:       List[str]   =					False
						_SCREAMING_SNAKE_CASE	:       List[str]   =					True
						_SCREAMING_SNAKE_CASE	:       str   =					False






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						SqueezeBertModelTester(self   )
												lowerCAmelCase__    						=						ConfigTester(self     ,						config_class=_UpperCamelCase     ,						dim=37   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_model(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCamelCase   )







						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__    						=						SqueezeBertModel.from_pretrained(_UpperCamelCase   )
																		self.assertIsNotNone(_UpperCamelCase   )




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






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'   )

												lowerCAmelCase__    						=						torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]   )
												lowerCAmelCase__    						=						model(_UpperCamelCase   )[0]
												lowerCAmelCase__    						=						torch.Size((1, 3)   )
												self.assertEqual(output.shape     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						torch.tensor([[0.64_01, -0.03_49, -0.60_41]]   )
												self.assertTrue(torch.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-4   )   )













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





