


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 		snake_case_(       a__			):






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Optional[int]=1_3					,				UpperCamelCase_  :					int=7					,				UpperCamelCase_  :					Dict=True					,				UpperCamelCase_  :					List[Any]=True					,				UpperCamelCase_  :					List[Any]=False					,				UpperCamelCase_  :					Optional[Any]=True					,				UpperCamelCase_  :					List[Any]=9_9					,				UpperCamelCase_  :					List[str]=3_2					,				UpperCamelCase_  :					List[str]=5					,				UpperCamelCase_  :					Union[str, Any]=4					,				UpperCamelCase_  :					Any=6_4					,				UpperCamelCase_  :					Union[str, Any]="gelu"					,				UpperCamelCase_  :					Dict=0.1					,				UpperCamelCase_  :					Any=0.1					,				UpperCamelCase_  :					Union[str, Any]=5_1_2					,				UpperCamelCase_  :					List[str]=1_6					,				UpperCamelCase_  :					Optional[Any]=2					,				UpperCamelCase_  :					List[Any]=0.02					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					Any=4					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					Union[str, Any]=2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					Union[str, Any]=2					,				UpperCamelCase_  :					Any=4					,				UpperCamelCase_  :					str=1					,				):
           lowerCAmelCase					:	Tuple       =   parent
           lowerCAmelCase					:	Dict       =   batch_size
           lowerCAmelCase					:	Optional[Any]       =   seq_length
           lowerCAmelCase					:	Union[str, Any]       =   is_training
           lowerCAmelCase					:	Dict       =   use_input_mask
           lowerCAmelCase					:	Any       =   use_token_type_ids
           lowerCAmelCase					:	Union[str, Any]       =   use_labels
           lowerCAmelCase					:	Optional[int]       =   vocab_size
           lowerCAmelCase					:	List[str]       =   hidden_size
           lowerCAmelCase					:	Tuple       =   num_hidden_layers
           lowerCAmelCase					:	Any       =   num_attention_heads
           lowerCAmelCase					:	int       =   intermediate_size
           lowerCAmelCase					:	Dict       =   hidden_act
           lowerCAmelCase					:	Dict       =   hidden_dropout_prob
           lowerCAmelCase					:	str       =   attention_probs_dropout_prob
           lowerCAmelCase					:	Union[str, Any]       =   max_position_embeddings
           lowerCAmelCase					:	int       =   type_vocab_size
           lowerCAmelCase					:	Union[str, Any]       =   type_sequence_label_size
           lowerCAmelCase					:	Any       =   initializer_range
           lowerCAmelCase					:	Dict       =   num_labels
           lowerCAmelCase					:	List[str]       =   num_choices
           lowerCAmelCase					:	str       =   scope
           lowerCAmelCase					:	Union[str, Any]       =   q_groups
           lowerCAmelCase					:	Union[str, Any]       =   k_groups
           lowerCAmelCase					:	Tuple       =   v_groups
           lowerCAmelCase					:	Optional[int]       =   post_attention_groups
           lowerCAmelCase					:	Dict       =   intermediate_groups
           lowerCAmelCase					:	Optional[int]       =   output_groups






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Union[str, Any]       =   ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size				)

           lowerCAmelCase					:	List[Any]       =   None
           if self.use_input_mask:
                 lowerCAmelCase					:	Union[str, Any]       =   random_attention_mask([self.batch_size, self.seq_length]				)

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

           lowerCAmelCase					:	Union[str, Any]       =   self.get_config()

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           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      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   SqueezeBertModel(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	int       =   model(UpperCamelCase_				)
           self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					List[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   SqueezeBertForMaskedLM(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	int       =   model(UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	int       =   SqueezeBertForQuestionAnswering(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	Dict       =   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      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Union[str, Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   self.num_labels
           lowerCAmelCase					:	int       =   SqueezeBertForSequenceClassification(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	Tuple       =   model(UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)				)






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Optional[int]       =   self.num_labels
           lowerCAmelCase					:	Union[str, Any]       =   SqueezeBertForTokenClassification(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()

           lowerCAmelCase					:	Any       =   model(UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)				)






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	str       =   self.num_choices
           lowerCAmelCase					:	Tuple       =   SqueezeBertForMultipleChoice(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	str       =   input_ids.unsqueeze(1				).expand(-1					,				self.num_choices					,				-1				).contiguous()
           lowerCAmelCase					:	Dict       =   input_mask.unsqueeze(1				).expand(-1					,				self.num_choices					,				-1				).contiguous()
           lowerCAmelCase					:	Tuple       =   model(
               UpperCamelCase_					,				attention_mask=UpperCamelCase_					,				labels=UpperCamelCase_					,				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Any       =   self.prepare_config_and_inputs()
           (lowerCAmelCase)					:	Dict       =   config_and_inputs
           lowerCAmelCase					:	str       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict



@require_torch
class 		snake_case_(       a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     __UpperCamelCase  =					(
         {
             '''feature-extraction''': SqueezeBertModel,
             '''fill-mask''': SqueezeBertForMaskedLM,
             '''question-answering''': SqueezeBertForQuestionAnswering,
             '''text-classification''': SqueezeBertForSequenceClassification,
             '''token-classification''': SqueezeBertForTokenClassification,
             '''zero-shot''': SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase  =					False
     __UpperCamelCase  =					True
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Tuple       =   SqueezeBertModelTester(self				)
           lowerCAmelCase					:	Any       =   ConfigTester(self					,				config_class=UpperCamelCase_					,				dim=3_7				)






     def      lowerCamelCase__     (	self  :					str				):
           self.config_tester.run_common_tests()






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	List[Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Tuple       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Tuple       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	int       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					List[Any]				):
           for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase					:	Union[str, Any]       =   SqueezeBertModel.from_pretrained(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)



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






     @slow
     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	List[str]       =   SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''				)

           lowerCAmelCase					:	List[Any]       =   torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]				)
           lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)[0]
           lowerCAmelCase					:	Tuple       =   torch.Size((1, 3)				)
           self.assertEqual(output.shape					,				UpperCamelCase_				)
           lowerCAmelCase					:	Dict       =   torch.tensor([[0.6_401, -0.0_349, -0.6_041]]				)
           self.assertTrue(torch.allclose(UpperCamelCase_					,				UpperCamelCase_					,				atol=1E-4				)				)






"""simple docstring"""





from __future__ import annotations



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   text, pattern
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   len(UpperCamelCase_				), len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					str				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if char == self.pattern[i]:
                       return i
           return -1






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if self.pattern[i] != self.text[current_pos + i]:
                       return current_pos + i
           return -1






     def      lowerCamelCase__     (	self  :					Dict				):
           # searches pattern in text and returns index positions
           lowerCAmelCase					:	Union[str, Any]       =   []
           for i in range(self.textLen - self.patLen + 1				):
                 lowerCAmelCase					:	str       =   self.mismatch_in_text(UpperCamelCase_				)
                 if mismatch_index == -1:
                       positions.append(UpperCamelCase_				)
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.match_in_pattern(self.text[mismatch_index]				)
                       lowerCAmelCase					:	int       =   (
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
           return positions



snake_case__		:     str    				=				'''ABAABA'''
snake_case__		:     List[str]    				=				'''AB'''
snake_case__		:     Union[str, Any]    				=				BoyerMooreSearch(text, pattern)
snake_case__		:     Optional[Any]    				=				bms.bad_character_heuristic()

if len(positions) == 0:
 print('''No match found''')
else:
 print('''Pattern found in following positions: ''')
 print(positions)


