

'''simple docstring'''

import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase       =     logging.get_logger(__name__)

UpperCAmelCase       =     {
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}


class      lowerCAmelCase   (					lowercase_      ):
       lowerCAmelCase_				=       """bart"""
       lowerCAmelCase_				=       ["""past_key_values"""]
       lowerCAmelCase_				=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







       def __init__(							self	:       Any  ,					__lowercase	:       List[str]=50265  ,					__lowercase	:       Optional[Any]=1024  ,					__lowercase	:       Any=12  ,					__lowercase	:       Dict=4096  ,					__lowercase	:       Any=16  ,					__lowercase	:       Tuple=12  ,					__lowercase	:       Dict=4096  ,					__lowercase	:       int=16  ,					__lowercase	:       Union[str, Any]=0.0  ,					__lowercase	:       Optional[int]=0.0  ,					__lowercase	:       str="gelu"  ,					__lowercase	:       Tuple=1024  ,					__lowercase	:       int=0.1  ,					__lowercase	:       Optional[int]=0.0  ,					__lowercase	:       Optional[int]=0.0  ,					__lowercase	:       Dict=0.0_2  ,					__lowercase	:       List[str]=0.0  ,					__lowercase	:       int=False  ,					__lowercase	:       List[str]=True  ,					__lowercase	:       Dict=3  ,					__lowercase	:       Optional[Any]=1  ,					__lowercase	:       str=0  ,					__lowercase	:       List[Any]=2  ,					__lowercase	:       int=True  ,					__lowercase	:       Any=2  ,					__lowercase	:       Optional[int]=2  ,					**__lowercase	:       Union[str, Any]  ,					):







            """simple docstring"""







            __lowercase							=vocab_size
            __lowercase							=max_position_embeddings
            __lowercase							=d_model
            __lowercase							=encoder_ffn_dim
            __lowercase							=encoder_layers
            __lowercase							=encoder_attention_heads
            __lowercase							=decoder_ffn_dim
            __lowercase							=decoder_layers
            __lowercase							=decoder_attention_heads
            __lowercase							=dropout
            __lowercase							=attention_dropout
            __lowercase							=activation_dropout
            __lowercase							=activation_function
            __lowercase							=init_std
            __lowercase							=encoder_layerdrop
            __lowercase							=decoder_layerdrop
            __lowercase							=classifier_dropout
            __lowercase							=use_cache
            __lowercase							=encoder_layers
            __lowercase							=scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                num_labels=__lowercase  ,					pad_token_id=__lowercase  ,					bos_token_id=__lowercase  ,					eos_token_id=__lowercase  ,					is_encoder_decoder=__lowercase  ,					decoder_start_token_id=__lowercase  ,					forced_eos_token_id=__lowercase  ,					**__lowercase  ,					)

            # ensure backward compatibility for BART CNN models
            if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'  ,					__lowercase					):
                 __lowercase							=self.bos_token_id
                 warnings.warn(
                     f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                     'The config can simply be saved and uploaded again to be fixed.'					)






class      lowerCAmelCase   (					lowercase_      ):







       @property
       def 	snake_case      (							self	:       Union[str, Any]					):







            """simple docstring"""







            if self.task in ["default", "seq2seq-lm"]:
                 __lowercase							=OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]					)

                 if self.use_past:
                      __lowercase							={0: 'batch'}
                      __lowercase							={0: 'batch', 1: 'past_decoder_sequence + sequence'}
                 else:
                      __lowercase							={0: 'batch', 1: 'decoder_sequence'}
                      __lowercase							={0: 'batch', 1: 'decoder_sequence'}

                 if self.use_past:
                      self.fill_with_past_key_values_(__lowercase  ,					direction='inputs'					)
            elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 __lowercase							=OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                     ]					)
                 if self.use_past:
                      __lowercase						,   __lowercase							=self.num_layers
                      for i in range(__lowercase					):
                           __lowercase							={0: 'batch', 2: 'past_sequence + sequence'}
                           __lowercase							={0: 'batch', 2: 'past_sequence + sequence'}
            else:
                 __lowercase							=OrderedDict(
                     [
                         ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                         ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                         ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                         ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                     ]					)

            return common_inputs







       @property
       def 	snake_case      (							self	:       Any					):







            """simple docstring"""







            if self.task in ["default", "seq2seq-lm"]:
                 __lowercase							=super().outputs
            else:
                 __lowercase							=super(__lowercase  ,					self					).outputs
                 if self.use_past:
                      __lowercase						,   __lowercase							=self.num_layers
                      for i in range(__lowercase					):
                           __lowercase							={0: 'batch', 2: 'past_sequence + sequence'}
                           __lowercase							={0: 'batch', 2: 'past_sequence + sequence'}
            return common_outputs







       def 	snake_case      (							self	:       int  ,					__lowercase	:       PreTrainedTokenizer  ,					__lowercase	:       int = -1  ,					__lowercase	:       int = -1  ,					__lowercase	:       bool = False  ,					__lowercase	:       Optional[TensorType] = None  ,					):







            """simple docstring"""







            __lowercase							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase					)

            # Generate decoder inputs
            __lowercase							=seq_length if not self.use_past else 1
            __lowercase							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase					)
            __lowercase							={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
            __lowercase							=dict(**__lowercase  ,					**__lowercase					)

            if self.use_past:
                 if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
                 else:
                      import torch
                 __lowercase						,   __lowercase							=common_inputs['input_ids'].shape
                 __lowercase							=common_inputs['decoder_input_ids'].shape[1]
                 __lowercase						,   __lowercase							=self.num_attention_heads
                 __lowercase							=(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 __lowercase							=decoder_seq_length + 3
                 __lowercase							=(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 __lowercase							=torch.cat(
                     [common_inputs['decoder_attention_mask'], torch.ones(__lowercase  ,					__lowercase					)]  ,					dim=1					)

                 __lowercase							=[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 __lowercase						,   __lowercase							=self.num_layers
                 __lowercase							=min(__lowercase  ,					__lowercase					)
                 __lowercase							=max(__lowercase  ,					__lowercase					) - min_num_layers
                 __lowercase							='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                 for _ in range(__lowercase					):
                      common_inputs["past_key_values"].append(
                          (
                              torch.zeros(__lowercase					),
                              torch.zeros(__lowercase					),
                              torch.zeros(__lowercase					),
                              torch.zeros(__lowercase					),
                          )					)
                 # TODO: test this.
                 __lowercase							=encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                 for _ in range(__lowercase  ,					__lowercase					):
                      common_inputs["past_key_values"].append((torch.zeros(__lowercase					), torch.zeros(__lowercase					))					)
            return common_inputs







       def 	snake_case      (							self	:       int  ,					__lowercase	:       PreTrainedTokenizer  ,					__lowercase	:       int = -1  ,					__lowercase	:       int = -1  ,					__lowercase	:       bool = False  ,					__lowercase	:       Optional[TensorType] = None  ,					):







            """simple docstring"""







            __lowercase							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase					)

            if self.use_past:
                 if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
                 else:
                      import torch
                 __lowercase						,   __lowercase							=common_inputs['input_ids'].shape
                 # Not using the same length for past_key_values
                 __lowercase							=seqlen + 2
                 __lowercase						,   __lowercase							=self.num_layers
                 __lowercase						,   __lowercase							=self.num_attention_heads
                 __lowercase							=(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 __lowercase							=common_inputs['attention_mask'].dtype
                 __lowercase							=torch.cat(
                     [common_inputs['attention_mask'], torch.ones(__lowercase  ,					__lowercase  ,					dtype=__lowercase					)]  ,					dim=1					)
                 __lowercase							=[
                     (torch.zeros(__lowercase					), torch.zeros(__lowercase					)) for _ in range(__lowercase					)
                 ]
            return common_inputs







       def 	snake_case      (							self	:       Union[str, Any]  ,					__lowercase	:       PreTrainedTokenizer  ,					__lowercase	:       int = -1  ,					__lowercase	:       int = -1  ,					__lowercase	:       bool = False  ,					__lowercase	:       Optional[TensorType] = None  ,					):







            """simple docstring"""







            __lowercase							=compute_effective_axis_dimension(
                __lowercase  ,					fixed_dimension=OnnxConfig.default_fixed_batch  ,					num_token_to_add=0					)

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            __lowercase							=tokenizer.num_special_tokens_to_add(__lowercase					)
            __lowercase							=compute_effective_axis_dimension(
                __lowercase  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=__lowercase					)

            # Generate dummy inputs according to compute batch and sequence
            __lowercase							=[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
            __lowercase							=dict(tokenizer(__lowercase  ,					return_tensors=__lowercase					)					)
            return common_inputs







       def 	snake_case      (							self	:       int  ,					__lowercase	:       PreTrainedTokenizer  ,					__lowercase	:       int = -1  ,					__lowercase	:       int = -1  ,					__lowercase	:       bool = False  ,					__lowercase	:       Optional[TensorType] = None  ,					):







            """simple docstring"""







            if self.task in ["default", "seq2seq-lm"]:
                 __lowercase							=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     __lowercase  ,					batch_size=__lowercase  ,					seq_length=__lowercase  ,					is_pair=__lowercase  ,					framework=__lowercase					)

            elif self.task == "causal-lm":
                 __lowercase							=self._generate_dummy_inputs_for_causal_lm(
                     __lowercase  ,					batch_size=__lowercase  ,					seq_length=__lowercase  ,					is_pair=__lowercase  ,					framework=__lowercase					)
            else:
                 __lowercase							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     __lowercase  ,					batch_size=__lowercase  ,					seq_length=__lowercase  ,					is_pair=__lowercase  ,					framework=__lowercase					)

            return common_inputs







       def 	snake_case      (							self	:       Tuple  ,					__lowercase	:       Tuple  ,					__lowercase	:       Tuple  ,					__lowercase	:       int  ,					__lowercase	:       str					):







            """simple docstring"""







            if self.task in ["default", "seq2seq-lm"]:
                 __lowercase							=super()._flatten_past_key_values_(__lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase					)
            else:
                 __lowercase							=super(__lowercase  ,					self					)._flatten_past_key_values_(
                     __lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase					)






'''simple docstring'''

import unittest

from transformers import DonutProcessor


lowerCamelCase							:   Tuple  						=	'naver-clova-ix/donut-base'
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= DonutProcessor.from_pretrained(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= {
                 '''name''': '''John Doe''',
                 '''age''': '''99''',
                 '''city''': '''Atlanta''',
                 '''state''': '''GA''',
                 '''zip''': '''30301''',
                 '''phone''': '''123-4567''',
                 '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
             }

             lowercase__      			= (
                 '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
                 '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
                 '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
                 '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
             )
             lowercase__      			= self.processor.tokenajson(UpperCamelCase )

             self.assertDictEqual(UpperCamelCase   ,						UpperCamelCase )




