import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
					from ... import PreTrainedTokenizerBase, TensorType

_lowerCAmelCase       =		logging.get_logger(__name__)
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "vision-encoder-decoder"
							UpperCAmelCase				   =     True







							def __init__(   self      :     Optional[Any] ,			**_A      :     Optional[int]		):
												super().__init__(**_A		)
												if "encoder" not in kwargs or "decoder" not in kwargs:
																	raise ValueError(
																	    F"""A configuraton of type {self.model_type} cannot be instantiated because """
																	    F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""		)

												_UpperCamelCase			     =     kwargs.pop('''encoder'''		)
												_UpperCamelCase			     =     encoder_config.pop('''model_type'''		)
												_UpperCamelCase			     =     kwargs.pop('''decoder'''		)
												_UpperCamelCase			     =     decoder_config.pop('''model_type'''		)

												_UpperCamelCase			     =     AutoConfig.for_model(_A ,			**_A		)
												_UpperCamelCase			     =     AutoConfig.for_model(_A ,			**_A		)
												_UpperCamelCase			     =     True







							@classmethod
							def  UpperCamelCase_						(   cls      :     Tuple ,			_A      :     PretrainedConfig ,			_A      :     PretrainedConfig ,			**_A      :     Union[str, Any]		):
												logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
												_UpperCamelCase			     =     True
												_UpperCamelCase			     =     True

												return cls(encoder=encoder_config.to_dict() ,			decoder=decoder_config.to_dict() ,			**_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.encoder.to_dict()
												_UpperCamelCase			     =     self.decoder.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output




class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     version.parse("1.11" )







							@property
							def  UpperCamelCase_						(   self      :     str		):
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ]		)







							@property
							def  UpperCamelCase_						(   self      :     Tuple		):
												return 1e-4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}		)




class     lowerCAmelCase_     (       __lowercase ):







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     OrderedDict()
												_UpperCamelCase			     =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												_UpperCamelCase			     =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												_UpperCamelCase			     =     {0: '''batch''', 1: '''encoder_sequence'''}

												return common_inputs







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     "PreTrainedTokenizerBase" ,			_A      :     int = -1 ,			_A      :     int = -1 ,			_A      :     bool = False ,			_A      :     Optional["TensorType"] = None ,			):
												import torch

												_UpperCamelCase			     =     OrderedDict()

												_UpperCamelCase			     =     super().generate_dummy_inputs(
												    _A ,			batch_size=_A ,			seq_length=_A ,			is_pair=_A ,			framework=_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     dummy_input['''input_ids'''].shape
												_UpperCamelCase			     =     (batch, encoder_sequence, self._config.encoder_hidden_size)
												_UpperCamelCase			     =     dummy_input.pop('''input_ids'''		)
												_UpperCamelCase			     =     dummy_input.pop('''attention_mask'''		)
												_UpperCamelCase			     =     torch.zeros(_A		)

												return common_inputs




class     lowerCAmelCase_     (       __lowercase ):







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												pass







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     PretrainedConfig		):
												return VisionEncoderDecoderEncoderOnnxConfig(_A		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     PretrainedConfig ,			_A      :     PretrainedConfig ,			_A      :     str = "default"		):
												_UpperCamelCase			     =     encoder_config.hidden_size
												return VisionEncoderDecoderDecoderOnnxConfig(_A ,			_A		)






'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

