



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


A__								=						logging.get_logger(__name__)

A__								=						{
    '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       a							(			lowercase__				):
		__lowerCAmelCase	:    Union[str, Any]											=					'''marian'''
		__lowerCAmelCase	:    Dict											=					['''past_key_values''']
		__lowerCAmelCase	:    Union[str, Any]											=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



		def __init__(     self     :List[str]   ,__lowercase     :List[str]=5_8_1_0_1   ,__lowercase     :Union[str, Any]=None   ,__lowercase     :Any=1_0_2_4   ,__lowercase     :Union[str, Any]=1_2   ,__lowercase     :Tuple=4_0_9_6   ,__lowercase     :List[Any]=1_6   ,__lowercase     :List[str]=1_2   ,__lowercase     :Tuple=4_0_9_6   ,__lowercase     :Optional[int]=1_6   ,__lowercase     :Optional[int]=0.0   ,__lowercase     :Tuple=0.0   ,__lowercase     :Union[str, Any]=True   ,__lowercase     :str=True   ,__lowercase     :Optional[Any]="gelu"   ,__lowercase     :Dict=1_0_2_4   ,__lowercase     :Tuple=0.1   ,__lowercase     :Any=0.0   ,__lowercase     :List[Any]=0.0   ,__lowercase     :Tuple=0.02   ,__lowercase     :str=5_8_1_0_0   ,__lowercase     :Optional[Any]=False   ,__lowercase     :Optional[Any]=5_8_1_0_0   ,__lowercase     :Dict=0   ,__lowercase     :Optional[Any]=0   ,__lowercase     :Optional[int]=True   ,**__lowercase     :List[str]   ,):
						snake_case__	:      Any     		=	vocab_size
						snake_case__	:      int     		=	decoder_vocab_size or vocab_size
						snake_case__	:      List[str]     		=	max_position_embeddings
						snake_case__	:      List[Any]     		=	d_model
						snake_case__	:      int     		=	encoder_ffn_dim
						snake_case__	:      List[str]     		=	encoder_layers
						snake_case__	:      Optional[Any]     		=	encoder_attention_heads
						snake_case__	:      List[str]     		=	decoder_ffn_dim
						snake_case__	:      Any     		=	decoder_layers
						snake_case__	:      List[Any]     		=	decoder_attention_heads
						snake_case__	:      List[Any]     		=	dropout
						snake_case__	:      Union[str, Any]     		=	attention_dropout
						snake_case__	:      Any     		=	activation_dropout
						snake_case__	:      List[str]     		=	activation_function
						snake_case__	:      str     		=	init_std
						snake_case__	:      int     		=	encoder_layerdrop
						snake_case__	:      Union[str, Any]     		=	decoder_layerdrop
						snake_case__	:      Any     		=	use_cache
						snake_case__	:      str     		=	encoder_layers
						snake_case__	:      Optional[int]     		=	scale_embedding  # scale factor will be sqrt(d_model) if True
						snake_case__	:      Tuple     		=	share_encoder_decoder_embeddings
						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__   ,eos_token_id=SCREAMING_SNAKE_CASE__   ,is_encoder_decoder=SCREAMING_SNAKE_CASE__   ,decoder_start_token_id=SCREAMING_SNAKE_CASE__   ,forced_eos_token_id=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,)


class       a							(			lowercase__				):



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def 	__lowerCamelCase			(     self     :Tuple  ):
						if self.task in ["default", "seq2seq-lm"]:
										snake_case__	:      str     		=	OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]  )

										if self.use_past:
														snake_case__	:      Tuple     		=	{0: 'batch'}
														snake_case__	:      List[str]     		=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
										else:
														snake_case__	:      Optional[Any]     		=	{0: 'batch', 1: 'decoder_sequence'}
														snake_case__	:      List[str]     		=	{0: 'batch', 1: 'decoder_sequence'}

										if self.use_past:
														self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__   ,direction='''inputs'''  )
						elif self.task == "causal-lm":
										# TODO: figure this case out.
										snake_case__	:      List[Any]     		=	OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]  )
										if self.use_past:
														snake_case__	:      Any     		=	self.num_layers
														for i in range(SCREAMING_SNAKE_CASE__  ):
																		snake_case__	:      Dict     		=	{0: 'batch', 2: 'past_sequence + sequence'}
																		snake_case__	:      Union[str, Any]     		=	{0: 'batch', 2: 'past_sequence + sequence'}
						else:
										snake_case__	:      Union[str, Any]     		=	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 	__lowerCamelCase			(     self     :Dict  ):
						if self.task in ["default", "seq2seq-lm"]:
										snake_case__	:      Optional[Any]     		=	super().outputs
						else:
										snake_case__	:      Optional[int]     		=	super(SCREAMING_SNAKE_CASE__   ,self  ).outputs
										if self.use_past:
														snake_case__	:      Optional[int]     		=	self.num_layers
														for i in range(SCREAMING_SNAKE_CASE__  ):
																		snake_case__	:      Optional[Any]     		=	{0: 'batch', 2: 'past_sequence + sequence'}
																		snake_case__	:      List[Any]     		=	{0: 'batch', 2: 'past_sequence + sequence'}
						return common_outputs



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :PreTrainedTokenizer   ,__lowercase     :int = -1   ,__lowercase     :int = -1   ,__lowercase     :bool = False   ,__lowercase     :Optional[TensorType] = None   ,):
						snake_case__	:      Optional[Any]     		=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )

						# Generate decoder inputs
						snake_case__	:      Tuple     		=	seq_length if not self.use_past else 1
						snake_case__	:      Union[str, Any]     		=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )
						snake_case__	:      Optional[int]     		=	{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
						snake_case__	:      Tuple     		=	dict(**SCREAMING_SNAKE_CASE__   ,**SCREAMING_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
										snake_case__	:      int     		=	common_inputs['input_ids'].shape
										snake_case__	:      Any     		=	common_inputs['decoder_input_ids'].shape[1]
										snake_case__	:      List[str]     		=	self.num_attention_heads
										snake_case__	:      List[Any]     		=	(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										snake_case__	:      Tuple     		=	decoder_seq_length + 3
										snake_case__	:      Tuple     		=	(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										snake_case__	:      Optional[Any]     		=	torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )]   ,dim=1  )

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

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



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :PreTrainedTokenizer   ,__lowercase     :int = -1   ,__lowercase     :int = -1   ,__lowercase     :bool = False   ,__lowercase     :Optional[TensorType] = None   ,):
						snake_case__	:      int     		=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_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
										snake_case__	:      Tuple     		=	common_inputs['input_ids'].shape
										# Not using the same length for past_key_values
										snake_case__	:      List[Any]     		=	seqlen + 2
										snake_case__	:      Optional[int]     		=	self.num_layers
										snake_case__	:      str     		=	self.num_attention_heads
										snake_case__	:      List[Any]     		=	(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

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



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :PreTrainedTokenizer   ,__lowercase     :int = -1   ,__lowercase     :int = -1   ,__lowercase     :bool = False   ,__lowercase     :Optional[TensorType] = 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
						snake_case__	:      Dict     		=	compute_effective_axis_dimension(
						    SCREAMING_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
						snake_case__	:      str     		=	tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__  )
						snake_case__	:      Union[str, Any]     		=	compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__   ,fixed_dimension=OnnxConfig.default_fixed_sequence   ,num_token_to_add=SCREAMING_SNAKE_CASE__  )

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



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :PreTrainedTokenizer   ,__lowercase     :int = -1   ,__lowercase     :int = -1   ,__lowercase     :bool = False   ,__lowercase     :Optional[TensorType] = None   ,):
						if self.task in ["default", "seq2seq-lm"]:
										snake_case__	:      Tuple     		=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    SCREAMING_SNAKE_CASE__   ,batch_size=SCREAMING_SNAKE_CASE__   ,seq_length=SCREAMING_SNAKE_CASE__   ,is_pair=SCREAMING_SNAKE_CASE__   ,framework=SCREAMING_SNAKE_CASE__  )

						else:
										snake_case__	:      Dict     		=	self._generate_dummy_inputs_for_causal_lm(
										    SCREAMING_SNAKE_CASE__   ,batch_size=SCREAMING_SNAKE_CASE__   ,seq_length=SCREAMING_SNAKE_CASE__   ,is_pair=SCREAMING_SNAKE_CASE__   ,framework=SCREAMING_SNAKE_CASE__  )

						return common_inputs



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :List[Any]   ,__lowercase     :Optional[int]   ,__lowercase     :Optional[int]   ,__lowercase     :Dict  ):
						if self.task in ["default", "seq2seq-lm"]:
										snake_case__	:      Dict     		=	super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )
						else:
										snake_case__	:      Optional[int]     		=	super(SCREAMING_SNAKE_CASE__   ,self  )._flatten_past_key_values_(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )






		@property
		def 	__lowerCamelCase			(     self     :int  ):
						return 1e-4





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


UpperCAmelCase_      : Optional[Any]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : str  					=       'T5Config'







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.array  ,    __A :			int  ,    __A :			int				)	->		jnp.ndarray:

       """simple docstring"""

       a_					:		Dict							=  jnp.zeros_like(__A				)
       a_					:		Dict							=  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
       a_					:		str							=  shifted_input_ids.at[:, 0].set(__A				)

       a_					:		int							=  jnp.where(shifted_input_ids == -1_00  ,    __A  ,    __A				)
       return shifted_input_ids
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     str        =						'''mt5'''
      snake_case__				:     List[Any]        =						MTaConfig
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     str        =						'''mt5'''
      snake_case__				:     List[str]        =						MTaConfig
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mt5'''
      snake_case__				:     Union[str, Any]        =						MTaConfig


