
from typing import Mapping

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


_lowerCamelCase :       List[str]     					=    logging.get_logger(__name__)

_lowerCamelCase :       Optional[int]     					=    {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[str]					     =      "longt5"
	_UpperCAmelCase :	Union[str, Any]					     =      ["past_key_values"]
	_UpperCAmelCase :	Any					     =      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(						self			:					Union[str, Any]    ,						lowercase			:					Union[str, Any]=32_128    ,						lowercase			:					Union[str, Any]=512    ,						lowercase			:					str=64    ,						lowercase			:					List[Any]=2_048    ,						lowercase			:					Any=6    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[str]=8    ,						lowercase			:					str=127    ,						lowercase			:					Tuple=16    ,						lowercase			:					int=32    ,						lowercase			:					Optional[int]=128    ,						lowercase			:					int=0.1    ,						lowercase			:					Tuple=1E-6    ,						lowercase			:					Tuple=1.0    ,						lowercase			:					Optional[int]="relu"    ,						lowercase			:					Optional[int]=True    ,						lowercase			:					int="local"    ,						lowercase			:					Dict=True    ,						lowercase			:					List[str]=0    ,						lowercase			:					List[str]=1    ,						**lowercase			:					Any    ,						):


				'''simple docstring'''


				_snake_case						=    vocab_size
				_snake_case						=    d_model
				_snake_case						=    d_kv
				_snake_case						=    d_ff
				_snake_case						=    num_layers
				# default = symmetry
				_snake_case						=    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				_snake_case						=    num_heads
				_snake_case						=    local_radius
				_snake_case						=    global_block_size
				_snake_case						=    relative_attention_num_buckets
				_snake_case						=    relative_attention_max_distance
				_snake_case						=    dropout_rate
				_snake_case						=    layer_norm_epsilon
				_snake_case						=    initializer_factor
				_snake_case						=    feed_forward_proj
				_snake_case						=    encoder_attention_type
				_snake_case						=    use_cache

				_snake_case						=    self.feed_forward_proj.split('-'			)
				_snake_case						=    act_info[-1]
				_snake_case						=    act_info[0] == 'gated'

				if len(lowercase			) > 1 and act_info[0] != "gated" or len(lowercase			) > 2:
							raise ValueError(
							    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
							    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
							    '\'gated-gelu\' or \'relu\''			)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							_snake_case						=    'gelu_new'

				super().__init__(
				    pad_token_id=lowercase    ,						eos_token_id=lowercase    ,						is_encoder_decoder=lowercase    ,						**lowercase    ,						)




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	@property
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    {
				    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
				    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
				}
				if self.use_past:
							_snake_case						=    'past_encoder_sequence + sequence'
							_snake_case						=    {0: 'batch'}
							_snake_case						=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
				else:
							_snake_case						=    {0: 'batch', 1: 'decoder_sequence'}
							_snake_case						=    {0: 'batch', 1: 'decoder_sequence'}

				if self.use_past:
							self.fill_with_past_key_values_(lowercase    ,						direction='inputs'			)

				return common_inputs





	@property
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				return 13

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)