

'''simple docstring'''
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  :		str  =		logging.get_logger(__name__)

_UpperCAmelCase  :		Any  =		{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'marian'
 UpperCamelCase__     =       ['past_key_values']
 UpperCamelCase__     =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







 def __init__(       self  , snake_case_=5_81_01  , snake_case_=None  , snake_case_=10_24  , snake_case_=12  , snake_case_=40_96  , snake_case_=16  , snake_case_=12  , snake_case_=40_96  , snake_case_=16  , snake_case_=0.0  , snake_case_=0.0  , snake_case_=True  , snake_case_=True  , snake_case_="gelu"  , snake_case_=10_24  , snake_case_=0.1  , snake_case_=0.0  , snake_case_=0.0  , snake_case_=0.02  , snake_case_=5_81_00  , snake_case_=False  , snake_case_=5_81_00  , snake_case_=0  , snake_case_=0  , snake_case_=True  , **snake_case_  , ):
   lowercase									=vocab_size
   lowercase									=decoder_vocab_size or 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									=use_cache
   lowercase									=encoder_layers
   lowercase									=scale_embedding  # scale factor will be sqrt(d_model) if True
   lowercase									=share_encoder_decoder_embeddings
   super().__init__(
       pad_token_id=snake_case_  , eos_token_id=snake_case_  , is_encoder_decoder=snake_case_  , decoder_start_token_id=snake_case_  , forced_eos_token_id=snake_case_  , **snake_case_  , )

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 						_A(       self   ):
   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_(snake_case_  , 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(snake_case_   ):
         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
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 						_A(       self   ):
   if self.task in ["default", "seq2seq-lm"]:
     lowercase									=super().outputs
   else:
     lowercase									=super(snake_case_  , self   ).outputs
     if self.use_past:
       lowercase							, lowercase									=self.num_layers
       for i in range(snake_case_   ):
         lowercase									={0: '''batch''', 2: '''past_sequence + sequence'''}
         lowercase									={0: '''batch''', 2: '''past_sequence + sequence'''}
   return common_outputs







 def 						_A(       self  , snake_case_  , snake_case_ = -1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , ):
   lowercase									=self._generate_dummy_inputs_for_encoder_and_decoder(
       snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   )

   # Generate decoder inputs
   lowercase									=seq_length if not self.use_past else 1
   lowercase									=self._generate_dummy_inputs_for_encoder_and_decoder(
       snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   )
   lowercase									={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
   lowercase									=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
     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(snake_case_  , snake_case_   )]  , 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(snake_case_  , snake_case_   )
     lowercase									=max(snake_case_  , snake_case_   ) - min_num_layers
     lowercase									='''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.
     lowercase									=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  , snake_case_  , snake_case_ = -1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , ):
   lowercase									=self._generate_dummy_inputs_for_encoder_and_decoder(
       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
     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(snake_case_  , snake_case_  , dtype=snake_case_   )]  , dim=1   )
     lowercase									=[
         (torch.zeros(snake_case_   ), torch.zeros(snake_case_   )) for _ in range(snake_case_   )
     ]
   return common_inputs







 def 						_A(       self  , snake_case_  , snake_case_ = -1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , ):
   # Copied from OnnxConfig.generate_dummy_inputs
   # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
   lowercase									=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
   lowercase									=tokenizer.num_special_tokens_to_add(snake_case_   )
   lowercase									=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
   lowercase									=[''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
   lowercase									=dict(tokenizer(snake_case_  , return_tensors=snake_case_   )   )
   return common_inputs







 def 						_A(       self  , snake_case_  , snake_case_ = -1  , snake_case_ = -1  , snake_case_ = False  , snake_case_ = None  , ):
   if self.task in ["default", "seq2seq-lm"]:
     lowercase									=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_   )

   else:
     lowercase									=self._generate_dummy_inputs_for_causal_lm(
         snake_case_  , batch_size=snake_case_  , seq_length=snake_case_  , is_pair=snake_case_  , framework=snake_case_   )

   return common_inputs







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







 @property
 def 						_A(       self   ):
   return 1E-4


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
