

"""simple docstring"""


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


__A						       =      logging.get_logger(__name__)

__A						       =      {
    "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    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  """gptj"""
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  {
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}

				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    str=5_0_4_0_0					,  UpperCamelCase__							:    Optional[Any]=2_0_4_8					,  UpperCamelCase__							:    str=4_0_9_6					,  UpperCamelCase__							:    Any=2_8					,  UpperCamelCase__							:    Dict=1_6					,  UpperCamelCase__							:    Union[str, Any]=6_4					,  UpperCamelCase__							:    List[Any]=None					,  UpperCamelCase__							:    List[Any]="gelu_new"					,  UpperCamelCase__							:    Optional[int]=0.0					,  UpperCamelCase__							:    Dict=0.0					,  UpperCamelCase__							:    str=0.0					,  UpperCamelCase__							:    List[str]=1e-5					,  UpperCamelCase__							:    Any=0.02					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    Optional[int]=5_0_2_5_6					,  UpperCamelCase__							:    Union[str, Any]=5_0_2_5_6					,  UpperCamelCase__							:    str=False					,  **UpperCamelCase__							:    Any					,  )->       List[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:	Tuple   				=      bos_token_id
						__lowerCAmelCase:	int   				=      eos_token_id

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





class    snake_case     (    __snake_case  ):

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    PretrainedConfig					,  UpperCamelCase__							:    str = "default"					,  UpperCamelCase__							:    List[PatchingSpec] = None					,  UpperCamelCase__							:    bool = False					,  )->       Tuple:



						'''simple docstring'''


						super().__init__(UpperCamelCase__					,  task=UpperCamelCase__					,  patching_specs=UpperCamelCase__					,  use_past=UpperCamelCase__)
						if not getattr(self._config					,  "pad_token_id"					,  UpperCamelCase__):
								# TODO: how to do that better?
								__lowerCAmelCase:	Dict   				=      0

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



						'''simple docstring'''


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

						return common_inputs

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



						'''simple docstring'''


						return self._config.n_layer

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



						'''simple docstring'''


						return self._config.n_head

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    PreTrainedTokenizer					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    int = -1					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    Optional[TensorType] = None					,  )->       Mapping[str, Any]:



						'''simple docstring'''


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

						# We need to order the input in the way they appears in the forward()
						__lowerCAmelCase:	Optional[int]   				=      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

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

						__lowerCAmelCase:	str   				=      common_inputs["attention_mask"]
						if self.use_past:
								__lowerCAmelCase:	Dict   				=      ordered_inputs["attention_mask"].dtype
								__lowerCAmelCase:	List[str]   				=      torch.cat(
								    [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__					,  UpperCamelCase__					,  dtype=UpperCamelCase__)]					,  dim=1)

						return ordered_inputs




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



						'''simple docstring'''


						return 1_3





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A						       =      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      ["SpeechT5Tokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SpeechT5ForSpeechToText",
			    "SpeechT5ForSpeechToSpeech",
			    "SpeechT5ForTextToSpeech",
			    "SpeechT5Model",
			    "SpeechT5PreTrainedModel",
			    "SpeechT5HifiGan",
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


