



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Dict			       =    "trocr"
					A__				:		List[Any]			       =    ["past_key_values"]
					A__				:		List[Any]			       =    {
					    "num_attention_heads": "decoder_attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "decoder_layers",
					}



					def __init__(     self					:       Tuple				,					_snake_case					:       Any=5_02_65				,					_snake_case					:       Optional[Any]=10_24				,					_snake_case					:       List[Any]=12				,					_snake_case					:       List[Any]=16				,					_snake_case					:       str=40_96				,					_snake_case					:       List[str]="gelu"				,					_snake_case					:       List[Any]=5_12				,					_snake_case					:       Dict=0.1				,					_snake_case					:       Any=0.0				,					_snake_case					:       str=0.0				,					_snake_case					:       List[str]=2				,					_snake_case					:       Tuple=0.02				,					_snake_case					:       int=0.0				,					_snake_case					:       Dict=True				,					_snake_case					:       List[str]=False				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Any=True				,					_snake_case					:       List[Any]=1				,					_snake_case					:       List[Any]=0				,					_snake_case					:       Union[str, Any]=2				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    vocab_size
								A__       =    d_model
								A__       =    decoder_layers
								A__       =    decoder_attention_heads
								A__       =    decoder_ffn_dim
								A__       =    activation_function
								A__       =    max_position_embeddings
								A__       =    dropout
								A__       =    attention_dropout
								A__       =    activation_dropout
								A__       =    init_std
								A__       =    decoder_layerdrop
								A__       =    use_cache
								A__       =    scale_embedding
								A__       =    use_learned_position_embeddings
								A__       =    layernorm_embedding

								super().__init__(
								    pad_token_id=_snake_case				,					bos_token_id=_snake_case				,					eos_token_id=_snake_case				,					decoder_start_token_id=_snake_case				,					**_snake_case				,					)









# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




