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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		int      			=   """perceiver"""







    def __init__(			self				,				__lowercase=256				,				__lowercase=1_280				,				__lowercase=768				,				__lowercase=1				,				__lowercase=26				,				__lowercase=8				,				__lowercase=8				,				__lowercase=None				,				__lowercase=None				,				__lowercase="kv"				,				__lowercase=1				,				__lowercase=1				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-1_2				,				__lowercase=True				,				__lowercase=262				,				__lowercase=2_048				,				__lowercase=56				,				__lowercase=[368, 496]				,				__lowercase=16				,				__lowercase=1_920				,				__lowercase=16				,				__lowercase=[1, 16, 224, 224]				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	num_latents
        __UpperCamelCase  :List[Any]    						=	d_latents
        __UpperCamelCase  :List[str]    						=	d_model
        __UpperCamelCase  :Tuple    						=	num_blocks
        __UpperCamelCase  :List[Any]    						=	num_self_attends_per_block
        __UpperCamelCase  :Any    						=	num_self_attention_heads
        __UpperCamelCase  :List[str]    						=	num_cross_attention_heads
        __UpperCamelCase  :Tuple    						=	qk_channels
        __UpperCamelCase  :int    						=	v_channels
        __UpperCamelCase  :List[str]    						=	cross_attention_shape_for_attention
        __UpperCamelCase  :str    						=	self_attention_widening_factor
        __UpperCamelCase  :Optional[int]    						=	cross_attention_widening_factor
        __UpperCamelCase  :List[str]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	attention_probs_dropout_prob
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	layer_norm_eps
        __UpperCamelCase  :Any    						=	use_query_residual
        # masked language modeling attributes
        __UpperCamelCase  :List[str]    						=	vocab_size
        __UpperCamelCase  :Any    						=	max_position_embeddings
        # image classification attributes
        __UpperCamelCase  :List[str]    						=	image_size
        # flow attributes
        __UpperCamelCase  :Optional[Any]    						=	train_size
        # multimodal autoencoding attributes
        __UpperCamelCase  :str    						=	num_frames
        __UpperCamelCase  :List[str]    						=	audio_samples_per_frame
        __UpperCamelCase  :int    						=	samples_per_patch
        __UpperCamelCase  :Optional[Any]    						=	output_shape


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    @property
    def     UpperCamelCase__     (			self) ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            __UpperCamelCase  :List[str]    						=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
            __UpperCamelCase  :Union[str, Any]    						=	{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''inputs''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ])







    @property
    def     UpperCamelCase__     (			self) ->			float:
        return 1E-4







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = -1				,				__lowercase = -1				,				__lowercase = -1				,				__lowercase = False				,				__lowercase = None				,				__lowercase = 3				,				__lowercase = 40				,				__lowercase = 40				,				) ->			Mapping[str, Any]:
        # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

        if isinstance(__lowercase				,				__lowercase):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            __UpperCamelCase  :Any    						=	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
            __UpperCamelCase  :Union[str, Any]    						=	preprocessor.num_special_tokens_to_add(__lowercase)
            __UpperCamelCase  :Union[str, Any]    						=	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
            __UpperCamelCase  :Tuple    						=	[''' '''.join(['''a''']) * seq_length] * batch_size
            __UpperCamelCase  :List[Any]    						=	dict(preprocessor(__lowercase				,				return_tensors=__lowercase))
            __UpperCamelCase  :Dict    						=	inputs.pop('''input_ids''')
            return inputs
        elif isinstance(__lowercase				,				__lowercase) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            __UpperCamelCase  :Dict    						=	compute_effective_axis_dimension(__lowercase				,				fixed_dimension=OnnxConfig.default_fixed_batch)
            __UpperCamelCase  :Union[str, Any]    						=	self._generate_dummy_images(__lowercase				,				__lowercase				,				__lowercase				,				__lowercase)
            __UpperCamelCase  :Tuple    						=	dict(preprocessor(images=__lowercase				,				return_tensors=__lowercase))
            __UpperCamelCase  :List[Any]    						=	inputs.pop('''pixel_values''')
            return inputs
        else:
            raise ValueError(
                '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''')






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 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





