


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
	from ...feature_extraction_utils import FeatureExtractionMixin
	from ...tokenization_utils_base import PreTrainedTokenizerBase
	from ...utils import TensorType

A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     Optional[Any]    					=    {
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
A  :     Optional[int]    					=    [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
A  :     Any    					=    [
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class 						_UpperCamelCase						(     __lowerCamelCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          ="""whisper"""
							__UpperCAmelCase		:					str          =["""past_key_values"""]
							__UpperCAmelCase		:					Any          ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

							def __init__(  self					,     __a=5_18_65					,     __a=80					,     __a=6					,     __a=4					,     __a=6					,     __a=4					,     __a=15_36					,     __a=15_36					,     __a=0.0					,     __a=0.0					,     __a=5_02_57					,     __a=True					,     __a=True					,     __a="gelu"					,     __a=2_56					,     __a=0.0					,     __a=0.0					,     __a=0.0					,     __a=0.0_2					,     __a=False					,     __a=15_00					,     __a=4_48					,     __a=5_02_56					,     __a=5_02_56					,     __a=5_02_56					,     __a=None					,     __a=[2_20, 5_02_56]					,     __a=False					,     __a=2_56					,     __a=False					,     __a=0.0_5					,     __a=10					,     __a=2					,     __a=0.0					,     __a=10					,     __a=0					,     __a=7					,     **__a					,     ):
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    num_mel_bins
										__lowerCAmelCase       					=    d_model
										__lowerCAmelCase       					=    encoder_layers
										__lowerCAmelCase       					=    encoder_attention_heads
										__lowerCAmelCase       					=    decoder_layers
										__lowerCAmelCase       					=    decoder_attention_heads
										__lowerCAmelCase       					=    decoder_ffn_dim
										__lowerCAmelCase       					=    encoder_ffn_dim
										__lowerCAmelCase       					=    dropout
										__lowerCAmelCase       					=    attention_dropout
										__lowerCAmelCase       					=    activation_dropout
										__lowerCAmelCase       					=    activation_function
										__lowerCAmelCase       					=    init_std
										__lowerCAmelCase       					=    encoder_layerdrop
										__lowerCAmelCase       					=    decoder_layerdrop
										__lowerCAmelCase       					=    use_cache
										__lowerCAmelCase       					=    encoder_layers
										__lowerCAmelCase       					=    scale_embedding  # scale factor will be sqrt(d_model) if True
										__lowerCAmelCase       					=    max_source_positions
										__lowerCAmelCase       					=    max_target_positions

										# Audio Classification-specific parameters. Feel free to ignore for other classes.
										__lowerCAmelCase       					=    classifier_proj_size
										__lowerCAmelCase       					=    use_weighted_layer_sum

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										__lowerCAmelCase       					=    apply_spec_augment
										__lowerCAmelCase       					=    mask_time_prob
										__lowerCAmelCase       					=    mask_time_length
										__lowerCAmelCase       					=    mask_time_min_masks
										__lowerCAmelCase       					=    mask_feature_prob
										__lowerCAmelCase       					=    mask_feature_length
										__lowerCAmelCase       					=    mask_feature_min_masks

										__lowerCAmelCase       					=    median_filter_width

										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_					,     bos_token_id=SCREAMING_SNAKE_CASE_					,     eos_token_id=SCREAMING_SNAKE_CASE_					,     is_encoder_decoder=SCREAMING_SNAKE_CASE_					,     decoder_start_token_id=SCREAMING_SNAKE_CASE_					,     suppress_tokens=SCREAMING_SNAKE_CASE_					,     begin_suppress_tokens=SCREAMING_SNAKE_CASE_					,     **SCREAMING_SNAKE_CASE_					,     )
class 						_UpperCamelCase						(     __lowerCamelCase	):
							'''simple docstring'''







							@property
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    OrderedDict(
										    [
										        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
										    ]			)
										if self.use_past:
													__lowerCAmelCase       					=    {0: "batch"}
										else:
													__lowerCAmelCase       					=    {0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_					,     direction="inputs"			)

										return common_inputs

							def     snake_case       (  self					,     __a					,     __a = -1					,     __a = -1					,     __a = False					,     __a = None					,     __a = 2_20_50					,     __a = 5.0					,     __a = 2_20					,     ):
										__lowerCAmelCase       					=    OrderedDict()
										__lowerCAmelCase       					=    OnnxConfig.generate_dummy_inputs(
										    self					,     preprocessor=preprocessor.feature_extractor					,     batch_size=SCREAMING_SNAKE_CASE_					,     framework=SCREAMING_SNAKE_CASE_					,     sampling_rate=SCREAMING_SNAKE_CASE_					,     time_duration=SCREAMING_SNAKE_CASE_					,     frequency=SCREAMING_SNAKE_CASE_					,     )
										__lowerCAmelCase       					=    encoder_inputs["input_features"].shape[2]
										__lowerCAmelCase       					=    encoder_sequence_length // 2 if self.use_past else seq_length

										__lowerCAmelCase       					=    super().generate_dummy_inputs(
										    preprocessor.tokenizer					,     SCREAMING_SNAKE_CASE_					,     SCREAMING_SNAKE_CASE_					,     SCREAMING_SNAKE_CASE_					,     SCREAMING_SNAKE_CASE_			)

										__lowerCAmelCase       					=    encoder_inputs.pop("input_features"			)
										__lowerCAmelCase       					=    decoder_inputs.pop("decoder_input_ids"			)

										if "past_key_values" in decoder_inputs:
													__lowerCAmelCase       					=    decoder_inputs.pop("past_key_values"			)

										return dummy_inputs



							@property
							def     snake_case       (  self			):
										return 1e-3








"""simple docstring"""


import flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




