

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  lowercase				(       lowercase_			):




				def __init__(					self      ,   snake_case      ,   snake_case=13      ,   snake_case=7      ,   snake_case=True      ,   snake_case=True      ,   snake_case=False      ,   snake_case=True      ,   snake_case=99      ,   snake_case=32      ,   snake_case=5      ,   snake_case=4      ,   snake_case=64      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=512      ,   snake_case=16      ,   snake_case=2      ,   snake_case=0.02      ,   snake_case=3      ,   snake_case=4      ,   snake_case=None      ,   snake_case=2      ,   snake_case=2      ,   snake_case=2      ,   snake_case=2      ,   snake_case=4      ,   snake_case=1      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    seq_length
											snake_case_							=    is_training
											snake_case_							=    use_input_mask
											snake_case_							=    use_token_type_ids
											snake_case_							=    use_labels
											snake_case_							=    vocab_size
											snake_case_							=    hidden_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    intermediate_size
											snake_case_							=    hidden_act
											snake_case_							=    hidden_dropout_prob
											snake_case_							=    attention_probs_dropout_prob
											snake_case_							=    max_position_embeddings
											snake_case_							=    type_vocab_size
											snake_case_							=    type_sequence_label_size
											snake_case_							=    initializer_range
											snake_case_							=    num_labels
											snake_case_							=    num_choices
											snake_case_							=    scope
											snake_case_							=    q_groups
											snake_case_							=    k_groups
											snake_case_							=    v_groups
											snake_case_							=    post_attention_groups
											snake_case_							=    intermediate_groups
											snake_case_							=    output_groups




				def     a							(					self       ):
											snake_case_							=    ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size       )

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

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

											snake_case_							=    self.get_config()

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




				def     a							(					self       ):
											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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    SqueezeBertModel(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(snake_case      ,   snake_case       )
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    SqueezeBertForMaskedLM(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(snake_case      ,   attention_mask=snake_case      ,   labels=snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    SqueezeBertForQuestionAnswering(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(
											    snake_case      ,   attention_mask=snake_case      ,   start_positions=snake_case      ,   end_positions=snake_case       )
											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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    SqueezeBertForSequenceClassification(snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(snake_case      ,   attention_mask=snake_case      ,   labels=snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    SqueezeBertForTokenClassification(config=snake_case       )
											model.to(snake_case       )
											model.eval()

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




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_choices
											snake_case_							=    SqueezeBertForMultipleChoice(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    input_ids.unsqueeze(1       ).expand(-1      ,   self.num_choices      ,   -1       ).contiguous()
											snake_case_							=    input_mask.unsqueeze(1       ).expand(-1      ,   self.num_choices      ,   -1       ).contiguous()
											snake_case_							=    model(
											    snake_case      ,   attention_mask=snake_case      ,   labels=snake_case      ,   )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices)       )




				def     a							(					self       ):
											snake_case_							=    self.prepare_config_and_inputs()
											((snake_case_)				,    (snake_case_)				,    (snake_case_)				,    (snake_case_)				,    (snake_case_)				,    (snake_case_))							=    config_and_inputs
											snake_case_							=    {'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_torch
class  lowercase				(       lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				__SCREAMING_SNAKE_CASE					: 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					: Union[str, Any]       			=			False
				__SCREAMING_SNAKE_CASE					: int       			=			True
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			False




				def     a							(					self       ):
											snake_case_							=    SqueezeBertModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   dim=37       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    SqueezeBertModel.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )







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




				@slow
				def     a							(					self       ):
											snake_case_							=    SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'       )

											snake_case_							=    torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]       )
											snake_case_							=    model(snake_case       )[0]
											snake_case_							=    torch.Size((1, 3)       )
											self.assertEqual(output.shape      ,   snake_case       )
											snake_case_							=    torch.tensor([[0.64_01, -0.03_49, -0.60_41]]       )
											self.assertTrue(torch.allclose(snake_case      ,   snake_case      ,   atol=1e-4       )       )







import os

import numpy
import onnx





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    a.name
							snake_case_							=    b.name

							snake_case_							=    ''
							snake_case_							=    ''

							snake_case_							=    a == b

							snake_case_							=    name_a
							snake_case_							=    name_b

							return res





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i, input_name in enumerate(node_proto.input      ):
														if input_name == name:
																					node_proto.input.insert(UpperCamelCase__   ,							UpperCamelCase__      )
																					node_proto.input.pop(i + 1      )

							if node_proto.op_type == "If":
														_graph_replace_input_with(node_proto.attribute[0].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														_graph_replace_input_with(node_proto.attribute[1].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							if node_proto.op_type == "Loop":
														_graph_replace_input_with(node_proto.attribute[0].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for n in graph_proto.node:
														_node_replace_input_with(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    list(model.graph.initializer      )
							snake_case_							=    list(model_without_ext.graph.initializer      )
							for i, ref_i in ind_to_replace:
														assert inits_with_data[i].name == inits[i].name
														assert inits_with_data[ref_i].name == inits[ref_i].name
														assert i > ref_i

														snake_case_							=    inits[i].name
														snake_case_							=    inits[ref_i].name

														model_without_ext.graph.initializer.remove(inits[i]      )

														# for n in model.graph.node:
														_graph_replace_input_with(model_without_ext.graph   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    os.path.dirname(UpperCamelCase__      )
							snake_case_							=    os.path.basename(UpperCamelCase__      )

							snake_case_							=    onnx.load(os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )      )

							snake_case_							=    list(model.graph.initializer      )

							snake_case_							=    set()
							snake_case_							=    {}
							snake_case_							=    []

							snake_case_							=    0

							for i in range(len(UpperCamelCase__      )      ):
														if i in dup_set:
																					continue

														for j in range(i + 1   ,							len(UpperCamelCase__      )      ):
																					if j in dup_set:
																												continue
																					if _is_equal_tensor_proto(inits[i]   ,							inits[j]      ):
																												dup_set.add(UpperCamelCase__      )
																												dup_set.add(UpperCamelCase__      )

																												snake_case_							=    inits[j].data_type
																												snake_case_							=    numpy.prod(inits[j].dims      )
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print('unexpected data type: '   ,							UpperCamelCase__      )
																												total_reduced_size += mem_size

																												snake_case_							=    inits[i].name
																												snake_case_							=    inits[j].name

																												if name_i in dup_map:
																																			dup_map[name_i].append(UpperCamelCase__      )
																												else:
																																			snake_case_							=    [name_j]
																												ind_to_replace.append((j, i)      )

							print('total reduced size: '   ,							total_reduced_size / 1024 / 1024 / 1024   ,							'GB'      )

							snake_case_							=    sorted(UpperCamelCase__      )
							_remove_dup_initializers_from_model(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    'optimized_' + model_file_name
							snake_case_							=    os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )
							onnx.save(UpperCamelCase__   ,							UpperCamelCase__      )

							return new_model




