from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   """gptj"""
    a__			:		List[Any]      			=   {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(			self				,				__lowercase=50_400				,				__lowercase=2_048				,				__lowercase=4_096				,				__lowercase=28				,				__lowercase=16				,				__lowercase=64				,				__lowercase=None				,				__lowercase="gelu_new"				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=1E-5				,				__lowercase=0.02				,				__lowercase=True				,				__lowercase=50_256				,				__lowercase=50_256				,				__lowercase=False				,				**__lowercase				,				) ->			Tuple:
        __UpperCamelCase  :Any    						=	vocab_size
        __UpperCamelCase  :int    						=	n_positions
        __UpperCamelCase  :Optional[int]    						=	n_embd
        __UpperCamelCase  :Optional[int]    						=	n_layer
        __UpperCamelCase  :Dict    						=	n_head
        __UpperCamelCase  :Union[str, Any]    						=	n_inner
        __UpperCamelCase  :int    						=	rotary_dim
        __UpperCamelCase  :Optional[int]    						=	activation_function
        __UpperCamelCase  :Dict    						=	resid_pdrop
        __UpperCamelCase  :Any    						=	embd_pdrop
        __UpperCamelCase  :Any    						=	attn_pdrop
        __UpperCamelCase  :int    						=	layer_norm_epsilon
        __UpperCamelCase  :str    						=	initializer_range
        __UpperCamelCase  :List[Any]    						=	use_cache

        __UpperCamelCase  :Any    						=	bos_token_id
        __UpperCamelCase  :str    						=	eos_token_id

        super().__init__(
            bos_token_id=__lowercase				,				eos_token_id=__lowercase				,				tie_word_embeddings=__lowercase				,				**__lowercase)


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase = "default"				,				__lowercase = None				,				__lowercase = False				,				) ->			int:
        super().__init__(__lowercase				,				task=__lowercase				,				patching_specs=__lowercase				,				use_past=__lowercase)
        if not getattr(self._config				,				'''pad_token_id'''				,				__lowercase):
            # TODO: how to do that better?
            __UpperCamelCase  :List[Any]    						=	0







    @property
    def     UpperCamelCase__     (			self) ->			Mapping[str, Mapping[int, str]]:
        __UpperCamelCase  :List[str]    						=	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
            self.fill_with_past_key_values_(__lowercase				,				direction='''inputs''')
            __UpperCamelCase  :Dict    						=	{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
            __UpperCamelCase  :int    						=	{0: '''batch''', 1: '''sequence'''}

        return common_inputs







    @property
    def     UpperCamelCase__     (			self) ->			int:
        return self._config.n_layer







    @property
    def     UpperCamelCase__     (			self) ->			int:
        return self._config.n_head







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = -1				,				__lowercase = -1				,				__lowercase = False				,				__lowercase = None				,				) ->			Mapping[str, Any]:
        __UpperCamelCase  :Union[str, Any]    						=	super(__lowercase				,				self).generate_dummy_inputs(
            __lowercase				,				batch_size=__lowercase				,				seq_length=__lowercase				,				is_pair=__lowercase				,				framework=__lowercase)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase  :Optional[Any]    						=	OrderedDict({'''input_ids''': common_inputs['''input_ids''']})

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
            else:
                import torch

                __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                __UpperCamelCase  :str    						=	seqlen + 2
                __UpperCamelCase  :List[Any]    						=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                __UpperCamelCase  :Optional[int]    						=	[
                    (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(self.num_layers)
                ]

        __UpperCamelCase  :Dict    						=	common_inputs['''attention_mask''']
        if self.use_past:
            __UpperCamelCase  :Optional[Any]    						=	ordered_inputs['''attention_mask'''].dtype
            __UpperCamelCase  :Tuple    						=	torch.cat(
                [ordered_inputs['''attention_mask'''], torch.ones(__lowercase				,				__lowercase				,				dtype=__lowercase)]				,				dim=1)

        return ordered_inputs







    @property
    def     UpperCamelCase__     (			self) ->			int:
        return 13






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





