


'''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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      Optional[Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[Any]=13 ,  lowerCAmelCase							:      List[Any]=7 ,  lowerCAmelCase							:      Optional[int]=True ,  lowerCAmelCase							:      Any=True ,  lowerCAmelCase							:      int=False ,  lowerCAmelCase							:      Union[str, Any]=True ,  lowerCAmelCase							:      Union[str, Any]=99 ,  lowerCAmelCase							:      str=32 ,  lowerCAmelCase							:      Any=5 ,  lowerCAmelCase							:      Tuple=4 ,  lowerCAmelCase							:      str=64 ,  lowerCAmelCase							:      Tuple="gelu" ,  lowerCAmelCase							:      Optional[Any]=0.1 ,  lowerCAmelCase							:      List[str]=0.1 ,  lowerCAmelCase							:      Any=5_12 ,  lowerCAmelCase							:      str=16 ,  lowerCAmelCase							:      Optional[int]=2 ,  lowerCAmelCase							:      Any=0.0_2 ,  lowerCAmelCase							:      int=3 ,  lowerCAmelCase							:      Union[str, Any]=4 ,  lowerCAmelCase							:      Union[str, Any]=None ,  lowerCAmelCase							:      Optional[int]=2 ,  lowerCAmelCase							:      List[Any]=2 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      Optional[Any]=2 ,  lowerCAmelCase							:      Union[str, Any]=4 ,  lowerCAmelCase							:      Optional[Any]=1 ,  ):
													A_			    =		parent
													A_			    =		batch_size
													A_			    =		seq_length
													A_			    =		is_training
													A_			    =		use_input_mask
													A_			    =		use_token_type_ids
													A_			    =		use_labels
													A_			    =		vocab_size
													A_			    =		hidden_size
													A_			    =		num_hidden_layers
													A_			    =		num_attention_heads
													A_			    =		intermediate_size
													A_			    =		hidden_act
													A_			    =		hidden_dropout_prob
													A_			    =		attention_probs_dropout_prob
													A_			    =		max_position_embeddings
													A_			    =		type_vocab_size
													A_			    =		type_sequence_label_size
													A_			    =		initializer_range
													A_			    =		num_labels
													A_			    =		num_choices
													A_			    =		scope
													A_			    =		q_groups
													A_			    =		k_groups
													A_			    =		v_groups
													A_			    =		post_attention_groups
													A_			    =		intermediate_groups
													A_			    =		output_groups






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

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

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

													A_			    =		self.get_config()

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






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






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






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






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Optional[int]	):
													A_			    =		self.num_labels
													A_			    =		SqueezeBertForTokenClassification(config=lowerCAmelCase	)
													model.to(lowerCAmelCase	)
													model.eval()

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






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






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.prepare_config_and_inputs()
													((A_)    ,					(A_)    ,					(A_)    ,					(A_)    ,					(A_)    ,					(A_))			    =		config_and_inputs
													A_			    =		{"input_ids": input_ids, "attention_mask": input_mask}
													return config, inputs_dict






@require_torch
class 					__lowerCAmelCase   (   lowercase				,			lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=(
						    (
						        SqueezeBertModel,
						        SqueezeBertForMaskedLM,
						        SqueezeBertForMultipleChoice,
						        SqueezeBertForQuestionAnswering,
						        SqueezeBertForSequenceClassification,
						        SqueezeBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						_UpperCAmelCase		:       Tuple   						=(
						    {
						        "feature-extraction": SqueezeBertModel,
						        "fill-mask": SqueezeBertForMaskedLM,
						        "question-answering": SqueezeBertForQuestionAnswering,
						        "text-classification": SqueezeBertForSequenceClassification,
						        "token-classification": SqueezeBertForTokenClassification,
						        "zero-shot": SqueezeBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_UpperCAmelCase		:       Dict   						=False
						_UpperCAmelCase		:       int   						=True
						_UpperCAmelCase		:       List[Any]   						=False






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													A_			    =		SqueezeBertModelTester(self	)
													A_			    =		ConfigTester(self ,  config_class=lowerCAmelCase ,  dim=37	)






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													self.config_tester.run_common_tests()






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_model(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCAmelCase	)






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






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

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"	)

													A_			    =		torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]	)
													A_			    =		model(lowerCAmelCase	)[0]
													A_			    =		torch.Size((1, 3)	)
													self.assertEqual(output.shape ,  lowerCAmelCase	)
													A_			    =		torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]	)
													self.assertTrue(torch.allclose(lowerCAmelCase ,  lowerCAmelCase ,  atol=1e-4	)	)









'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

							if tensor.shape == tf.TensorShape(UpperCamelCase_ ):
														return dynamic

							A_			    =		tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_ )]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														A_			    =		[1] * inputs.shape.rank
														A_			    =		shape_list(UpperCamelCase_ )[axis]
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = "input_ids" ):
							tf.debugging.assert_less(
							    UpperCamelCase_						,  tf.cast(UpperCamelCase_						,  dtype=tensor.dtype )						,  message=(
							        f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_ )}) must be smaller than the embedding "
							        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
							    )						,  )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
														    f"bytes: {bad_attributes}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





