
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


lowerCamelCase__ :       Union[str, Any]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Tuple						     =			{
    '''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 				lowercase__(				a__   ):
	'''simple docstring'''

	UpperCamelCase				=  """gptj"""
	UpperCamelCase				=  {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(						self    :Tuple		, lowerCamelCase_    :int=5_04_00		, lowerCamelCase_    :Any=20_48		, lowerCamelCase_    :int=40_96		, lowerCamelCase_    :str=28		, lowerCamelCase_    :str=16		, lowerCamelCase_    :Dict=64		, lowerCamelCase_    :Tuple=None		, lowerCamelCase_    :Optional[Any]="gelu_new"		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :List[Any]=0.0		, lowerCamelCase_    :str=0.0		, lowerCamelCase_    :Any=1E-5		, lowerCamelCase_    :Dict=0.0_2		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Optional[Any]=5_02_56		, lowerCamelCase_    :Optional[int]=5_02_56		, lowerCamelCase_    :str=False		, **lowerCamelCase_    :List[Any]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= vocab_size
			SCREAMING_SNAKE_CASE       :     Tuple				= n_positions
			SCREAMING_SNAKE_CASE       :     List[Any]				= n_embd
			SCREAMING_SNAKE_CASE       :     int				= n_layer
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= n_head
			SCREAMING_SNAKE_CASE       :     Optional[int]				= n_inner
			SCREAMING_SNAKE_CASE       :     Any				= rotary_dim
			SCREAMING_SNAKE_CASE       :     Any				= activation_function
			SCREAMING_SNAKE_CASE       :     Optional[int]				= resid_pdrop
			SCREAMING_SNAKE_CASE       :     Optional[int]				= embd_pdrop
			SCREAMING_SNAKE_CASE       :     int				= attn_pdrop
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= layer_norm_epsilon
			SCREAMING_SNAKE_CASE       :     Optional[int]				= initializer_range
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= use_cache

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= bos_token_id
			SCREAMING_SNAKE_CASE       :     Dict				= eos_token_id

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





class 				lowercase__(				a__   ):
	'''simple docstring'''

	def __init__(						self    :str		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Union[str, Any] = "default"		, lowerCamelCase_    :List[Any] = None		, lowerCamelCase_    :Optional[int] = False		, )							->			Dict:







			'''simple docstring'''




			super().__init__(lowerCAmelCase__		, task=lowerCAmelCase__		, patching_specs=lowerCAmelCase__		, use_past=lowerCAmelCase__					)
			if not getattr(self._config		, '''pad_token_id'''		, lowerCAmelCase__					):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
			if self.use_past:
					self.fill_with_past_key_values_(lowerCAmelCase__		, direction='''inputs'''					)
					SCREAMING_SNAKE_CASE       :     Tuple				= {0: "batch", 1: "past_sequence + sequence"}
			else:
					SCREAMING_SNAKE_CASE       :     List[Any]				= {0: "batch", 1: "sequence"}

			return common_inputs





	@property
	def   __lowerCAmelCase (						self    :Tuple					)							->			int:







			'''simple docstring'''




			return self._config.n_layer





	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			return self._config.n_head





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any] = -1		, lowerCamelCase_    :Union[str, Any] = -1		, lowerCamelCase_    :Any = False		, lowerCamelCase_    :Union[str, Any] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= super(lowerCAmelCase__		, self					).generate_dummy_inputs(
			    lowerCAmelCase__		, batch_size=lowerCAmelCase__		, seq_length=lowerCAmelCase__		, is_pair=lowerCAmelCase__		, framework=lowerCAmelCase__					)

			# We need to order the input in the way they appears in the forward()
			SCREAMING_SNAKE_CASE       :     Dict				= 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

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

			SCREAMING_SNAKE_CASE       :     List[str]				= common_inputs["attention_mask"]
			if self.use_past:
					SCREAMING_SNAKE_CASE       :     List[str]				= ordered_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.cat(
					    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase__		, lowerCAmelCase__		, dtype=lowerCAmelCase__					)]		, dim=1					)

			return ordered_inputs







	@property
	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			return 13







"""simple docstring"""






import math
from typing import Optional

import numpy as np

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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Any						     =			{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= use_conv_shortcut

			if self.norm_type not in ["weight_norm", "time_group_norm"]:
					raise ValueError(
					    f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"					)

			super().__init__(**lowerCamelCase_					)





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None or self.overlap is None:
					return None
			else:
					return max(1		, int((1.0 - self.overlap) * self.chunk_length					)					)





	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





