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/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}





class   lowerCAmelCase    (       lowercase_      ):
     _SCREAMING_SNAKE_CASE  :  str		="""mbart"""
     _SCREAMING_SNAKE_CASE  :  str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE  :  Tuple		={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(       self					,   lowerCAmelCase__=50265					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=12					,   lowerCAmelCase__=4096					,   lowerCAmelCase__=16					,   lowerCAmelCase__=12					,   lowerCAmelCase__=4096					,   lowerCAmelCase__=16					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=True					,   lowerCAmelCase__="gelu"					,   lowerCAmelCase__=1024					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=False					,   lowerCAmelCase__=1					,   lowerCAmelCase__=0					,   lowerCAmelCase__=2					,   lowerCAmelCase__=2					,   **lowerCAmelCase__					,   ):
            _A=						vocab_size
            _A=						max_position_embeddings
            _A=						d_model
            _A=						encoder_ffn_dim
            _A=						encoder_layers
            _A=						encoder_attention_heads
            _A=						decoder_ffn_dim
            _A=						decoder_layers
            _A=						decoder_attention_heads
            _A=						dropout
            _A=						attention_dropout
            _A=						activation_dropout
            _A=						activation_function
            _A=						init_std
            _A=						encoder_layerdrop
            _A=						decoder_layerdrop
            _A=						classifier_dropout
            _A=						use_cache
            _A=						encoder_layers
            _A=						scale_embedding  # scale factor will be sqrt(d_model) if True
            super().__init__(
                pad_token_id=snake_case__					,   bos_token_id=snake_case__					,   eos_token_id=snake_case__					,   is_encoder_decoder=snake_case__					,   forced_eos_token_id=snake_case__					,   **snake_case__					,   )





class   lowerCAmelCase    (       lowercase_      ):



     @property
     def       a__	(       self							):



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

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

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



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



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   ):
            _A=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                snake_case__					,   snake_case__					,   snake_case__					,   snake_case__					,   snake_case__							)

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

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

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

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

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



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   ):
            _A=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                snake_case__					,   snake_case__					,   snake_case__					,   snake_case__					,   snake_case__							)

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

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



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   ):
            _A=						compute_effective_axis_dimension(
                snake_case__					,   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
            _A=						tokenizer.num_special_tokens_to_add(snake_case__							)
            _A=						compute_effective_axis_dimension(
                snake_case__					,   fixed_dimension=OnnxConfig.default_fixed_sequence					,   num_token_to_add=snake_case__							)

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



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   ):



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

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

            return common_inputs





     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):



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







from typing import Literal

lowerCamelCase					    =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						LETTERS if mode == 'decrypt' else key
    _lowercase     			=						key if mode == 'decrypt' else LETTERS
    _lowercase     			=						''
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            _lowercase     			=						chars_a.find(symbol.upper()   )
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'encrypt'   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'decrypt'   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'Hello World'
    _lowercase     			=						'QWERTYUIOPASDFGHJKLZXCVBNM'
    _lowercase     			=						'decrypt'  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        _lowercase     			=						encrypt_message(_lowercase,   _lowercase   )
    elif mode == "decrypt":
        _lowercase     			=						decrypt_message(_lowercase,   _lowercase   )
    print(f'''Using the key {key}, the {mode}ed message is: {translated}'''   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()
