




import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
						    SpeechaTextaDecoder,
						    SpeechaTextaForCausalLM,
						)







@require_torch
class 				UpperCAmelCase:



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase=99	,				lowerCamelCase=13	,				lowerCamelCase=16	,				lowerCamelCase=7	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=False	,				lowerCamelCase=True	,				lowerCamelCase=2	,				lowerCamelCase=32	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=30	,				lowerCamelCase=0	,				lowerCamelCase=1	,				lowerCamelCase=2	,				lowerCamelCase=None	,				) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	Dict        =							parent
								lowercase__  :	str        =							batch_size
								lowercase__  :	str        =							decoder_seq_length
								# For common tests
								lowercase__  :	Union[str, Any]        =							self.decoder_seq_length
								lowercase__  :	List[Any]        =							is_training
								lowercase__  :	List[str]        =							use_attention_mask
								lowercase__  :	Any        =							use_labels

								lowercase__  :	Union[str, Any]        =							vocab_size
								lowercase__  :	List[Any]        =							d_model
								lowercase__  :	Tuple        =							d_model
								lowercase__  :	str        =							decoder_layers
								lowercase__  :	Any        =							decoder_layers
								lowercase__  :	Optional[Any]        =							decoder_ffn_dim
								lowercase__  :	Optional[int]        =							decoder_attention_heads
								lowercase__  :	int        =							decoder_attention_heads
								lowercase__  :	Dict        =							eos_token_id
								lowercase__  :	int        =							bos_token_id
								lowercase__  :	Optional[int]        =							pad_token_id
								lowercase__  :	Optional[Any]        =							decoder_start_token_id
								lowercase__  :	Tuple        =							use_cache
								lowercase__  :	List[Any]        =							max_position_embeddings

								lowercase__  :	Tuple        =							None
								lowercase__  :	List[Any]        =							decoder_seq_length
								lowercase__  :	List[str]        =							2
								lowercase__  :	List[str]        =							1

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							ids_tensor([self.batch_size, self.decoder_seq_length]	,				self.vocab_size    )

								lowercase__  :	Dict        =							None
								if self.use_attention_mask:
															lowercase__  :	Any        =							ids_tensor([self.batch_size, self.decoder_seq_length]	,				vocab_size=2    )

								lowercase__  :	Union[str, Any]        =							None
								if self.use_labels:
															lowercase__  :	int        =							ids_tensor([self.batch_size, self.decoder_seq_length]	,				self.vocab_size    )

								lowercase__  :	List[Any]        =							SpeechaTextaConfig(
								    vocab_size=self.vocab_size	,				d_model=self.d_model	,				decoder_layers=self.decoder_layers	,				decoder_ffn_dim=self.decoder_ffn_dim	,				decoder_attention_heads=self.decoder_attention_heads	,				eos_token_id=self.eos_token_id	,				bos_token_id=self.bos_token_id	,				use_cache=self.use_cache	,				pad_token_id=self.pad_token_id	,				decoder_start_token_id=self.decoder_start_token_id	,				max_position_embeddings=self.max_position_embeddings	,				)

								return (
								    config,
								    input_ids,
								    attention_mask,
								    lm_labels,
								)

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Any        =							True
								lowercase__  :	Optional[int]        =							SpeechaTextaDecoder(config=lowerCamelCase    ).to(lowerCamelCase    ).eval()
								lowercase__  :	Dict        =							input_ids[:2]

								input_ids[input_ids == 0] += 1
								# first forward pass
								lowercase__  :	List[str]        =							model(lowerCamelCase	,				use_cache=lowerCamelCase    )
								lowercase__  :	Any        =							model(lowerCamelCase    )
								lowercase__  :	Dict        =							model(lowerCamelCase	,				use_cache=lowerCamelCase    )

								self.parent.assertTrue(len(lowerCamelCase    ) == len(lowerCamelCase    )    )
								self.parent.assertTrue(len(lowerCamelCase    ) == len(lowerCamelCase    ) + 1    )

								lowercase__  :	Optional[int]        =							outputs["past_key_values"]

								# create hypothetical next token and extent to next_input_ids
								lowercase__  :	str        =							ids_tensor((2, 1)	,				config.vocab_size - 1    ) + 1

								# append to next input_ids and
								lowercase__  :	str        =							torch.cat([input_ids, next_tokens]	,				dim=-1    )

								print(lowerCamelCase    )

								lowercase__  :	List[Any]        =							model(lowerCamelCase    )["last_hidden_state"]
								lowercase__  :	List[Any]        =							model(lowerCamelCase	,				past_key_values=lowerCamelCase    )["last_hidden_state"]

								# select random slice
								lowercase__  :	Dict        =							ids_tensor((1,)	,				output_from_past.shape[-1]    ).item()
								lowercase__  :	str        =							output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
								lowercase__  :	List[Any]        =							output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								assert torch.allclose(lowerCamelCase	,				lowerCamelCase	,				atol=1E-3    )







	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	Any        =							self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       
								)  :	List[str]        =							config_and_inputs

								lowercase__  :	List[Any]        =							{
								    "input_ids": input_ids,
								    "attention_mask": attention_mask,
								}
								return config, inputs_dict







@require_torch
class 				UpperCAmelCase(					snake_case_   ,     snake_case_   ,     snake_case_   ,     unittest.TestCase					):



	"""simple docstring"""






	a     : Optional[int]	  =					(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
	a     : str	  =					(SpeechaTextaForCausalLM,) if is_torch_available() else ()
	a     : Tuple	  =					{"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
	a     : Dict	  =					True
	a     : int	  =					False

	def    __a     (					self	,				) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							SpeechaTextaStandaloneDecoderModelTester(self	,				is_training=lowerCamelCase    )
								lowercase__  :	Optional[int]        =							ConfigTester(self	,				config_class=lowerCamelCase    )

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								pass

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								pass

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								pass

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								self.config_tester.run_common_tests()

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Any        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase    )







	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								return





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )