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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """marian"""
    a__	:						str						=      ["""past_key_values"""]
    a__	:						str						=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__(	self , __lowercase=58_101 , __lowercase=None , __lowercase=1_024 , __lowercase=12 , __lowercase=4_096 , __lowercase=16 , __lowercase=12 , __lowercase=4_096 , __lowercase=16 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=True , __lowercase=True , __lowercase="gelu" , __lowercase=1_024 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=58_100 , __lowercase=False , __lowercase=58_100 , __lowercase=0 , __lowercase=0 , __lowercase=True , **__lowercase , )       ->	Union[str, Any]:
        __UpperCamelCase     :List[Any]			  =		vocab_size
        __UpperCamelCase     :Dict			  =		decoder_vocab_size or vocab_size
        __UpperCamelCase     :Union[str, Any]			  =		max_position_embeddings
        __UpperCamelCase     :List[Any]			  =		d_model
        __UpperCamelCase     :Optional[int]			  =		encoder_ffn_dim
        __UpperCamelCase     :Tuple			  =		encoder_layers
        __UpperCamelCase     :Union[str, Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Dict			  =		decoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		decoder_layers
        __UpperCamelCase     :Union[str, Any]			  =		decoder_attention_heads
        __UpperCamelCase     :int			  =		dropout
        __UpperCamelCase     :List[str]			  =		attention_dropout
        __UpperCamelCase     :List[Any]			  =		activation_dropout
        __UpperCamelCase     :Tuple			  =		activation_function
        __UpperCamelCase     :Union[str, Any]			  =		init_std
        __UpperCamelCase     :List[str]			  =		encoder_layerdrop
        __UpperCamelCase     :List[Any]			  =		decoder_layerdrop
        __UpperCamelCase     :str			  =		use_cache
        __UpperCamelCase     :Dict			  =		encoder_layers
        __UpperCamelCase     :Dict			  =		scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCamelCase     :Union[str, Any]			  =		share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=__lowercase , eos_token_id=__lowercase , is_encoder_decoder=__lowercase , decoder_start_token_id=__lowercase , forced_eos_token_id=__lowercase , **__lowercase , )


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






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

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

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = -1 , __lowercase = -1 , __lowercase = False , __lowercase = None , )       ->	Mapping[str, Any]:
        __UpperCamelCase     :Dict			  =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)

        # Generate decoder inputs
        __UpperCamelCase     :str			  =		seq_length if not self.use_past else 1
        __UpperCamelCase     :List[str]			  =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)
        __UpperCamelCase     :List[Any]			  =		{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        __UpperCamelCase     :Union[str, Any]			  =		dict(**__lowercase , **__lowercase)

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

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

            __UpperCamelCase     :Union[str, Any]			  =		[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.num_layers
            __UpperCamelCase     :Dict			  =		min(__lowercase , __lowercase)
            __UpperCamelCase     :Dict			  =		max(__lowercase , __lowercase) - min_num_layers
            __UpperCamelCase     :str			  =		'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = -1 , __lowercase = -1 , __lowercase = False , __lowercase = None , )       ->	Mapping[str, Any]:
        __UpperCamelCase     :int			  =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)

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

            __UpperCamelCase     :Optional[Any]			  =		common_inputs['''attention_mask'''].dtype
            __UpperCamelCase     :int			  =		torch.cat(
                [common_inputs['''attention_mask'''], torch.ones(__lowercase , __lowercase , dtype=__lowercase)] , dim=1)
            __UpperCamelCase     :Union[str, Any]			  =		[
                (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(__lowercase)
            ]
        return common_inputs







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = -1 , __lowercase = -1 , __lowercase = False , __lowercase = None , )       ->	Mapping[str, Any]:
        # 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
        __UpperCamelCase     :Optional[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     :Optional[int]			  =		tokenizer.num_special_tokens_to_add(__lowercase)
        __UpperCamelCase     :List[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     :Any			  =		[''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
        __UpperCamelCase     :Optional[Any]			  =		dict(tokenizer(__lowercase , return_tensors=__lowercase))
        return common_inputs







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = -1 , __lowercase = -1 , __lowercase = False , __lowercase = None , )       ->	Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase     :Optional[int]			  =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                __lowercase , batch_size=__lowercase , seq_length=__lowercase , is_pair=__lowercase , framework=__lowercase)

        else:
            __UpperCamelCase     :Dict			  =		self._generate_dummy_inputs_for_causal_lm(
                __lowercase , batch_size=__lowercase , seq_length=__lowercase , is_pair=__lowercase , framework=__lowercase)

        return common_inputs







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase)       ->	List[str]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase     :Optional[int]			  =		super()._flatten_past_key_values_(__lowercase , __lowercase , __lowercase , __lowercase)
        else:
            __UpperCamelCase     :List[str]			  =		super(__lowercase , self)._flatten_past_key_values_(
                __lowercase , __lowercase , __lowercase , __lowercase)







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







import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

