




import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__   ):




    """simple docstring"""

    @register_to_config
    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int = 128       ,     SCREAMING_SNAKE_CASE__					:					int = 256       ,     SCREAMING_SNAKE_CASE__					:					float = 2_000.0       ,     SCREAMING_SNAKE_CASE__					:					int = 768       ,     SCREAMING_SNAKE_CASE__					:					int = 12       ,     SCREAMING_SNAKE_CASE__					:					int = 12       ,     SCREAMING_SNAKE_CASE__					:					int = 64       ,     SCREAMING_SNAKE_CASE__					:					int = 2_048       ,     SCREAMING_SNAKE_CASE__					:					float = 0.1       ,     )				->	Union[str, Any]:
         super().__init__()

         lowerCAmelCase__          =			nn.Sequential(
             nn.Linear(SCREAMING_SNAKE_CASE__       ,     d_model * 4       ,     bias=SCREAMING_SNAKE_CASE__       )       ,     nn.SiLU()       ,     nn.Linear(d_model * 4       ,     d_model * 4       ,     bias=SCREAMING_SNAKE_CASE__       )       ,     nn.SiLU()       ,     )

         lowerCAmelCase__          =			nn.Embedding(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			False

         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			nn.Dropout(p=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			nn.ModuleList()
         for lyr_num in range(SCREAMING_SNAKE_CASE__       ):
              # FiLM conditional T5 decoder
              lowerCAmelCase__          =			DecoderLayer(d_model=SCREAMING_SNAKE_CASE__       ,     d_kv=SCREAMING_SNAKE_CASE__       ,     num_heads=SCREAMING_SNAKE_CASE__       ,     d_ff=SCREAMING_SNAKE_CASE__       ,     dropout_rate=SCREAMING_SNAKE_CASE__       )
              self.decoders.append(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			TaLayerNorm(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			nn.Dropout(p=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	Optional[int]:
         lowerCAmelCase__          =			torch.mul(query_input.unsqueeze(-1       )       ,     key_input.unsqueeze(-2       )       )
         return mask.unsqueeze(-3       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	str:
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         lowerCAmelCase__          =			get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time       ,     embedding_dim=self.config.d_model       ,     max_period=self.config.max_decoder_noise_time       ,     ).to(dtype=self.dtype       )

         lowerCAmelCase__          =			self.conditioning_emb(SCREAMING_SNAKE_CASE__       ).unsqueeze(1       )

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         lowerCAmelCase__          =			decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         lowerCAmelCase__          =			torch.broadcast_to(
             torch.arange(SCREAMING_SNAKE_CASE__       ,     device=decoder_input_tokens.device       )       ,     (batch, seq_length)       ,     )

         lowerCAmelCase__          =			self.position_encoding(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.continuous_inputs_projection(SCREAMING_SNAKE_CASE__       )
         inputs += position_encodings
         lowerCAmelCase__          =			self.dropout(SCREAMING_SNAKE_CASE__       )

         # decoder: No padding present.
         lowerCAmelCase__          =			torch.ones(
             decoder_input_tokens.shape[:2]       ,     device=decoder_input_tokens.device       ,     dtype=inputs.dtype       )

         # Translate encoding masks to encoder-decoder masks.
         lowerCAmelCase__          =			[(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         lowerCAmelCase__          =			torch.cat([x[0] for x in encodings_and_encdec_masks]       ,     dim=1       )
         lowerCAmelCase__          =			torch.cat([x[1] for x in encodings_and_encdec_masks]       ,     dim=-1       )

         for lyr in self.decoders:
              lowerCAmelCase__          =			lyr(
                  SCREAMING_SNAKE_CASE__       ,     conditioning_emb=SCREAMING_SNAKE_CASE__       ,     encoder_hidden_states=SCREAMING_SNAKE_CASE__       ,     encoder_attention_mask=SCREAMING_SNAKE_CASE__       ,     )[0]

         lowerCAmelCase__          =			self.decoder_norm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.post_dropout(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.spec_out(SCREAMING_SNAKE_CASE__       )
         return spec_out






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str=1e-6       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=SCREAMING_SNAKE_CASE__       ,     d_kv=SCREAMING_SNAKE_CASE__       ,     num_heads=SCREAMING_SNAKE_CASE__       ,     dropout_rate=SCREAMING_SNAKE_CASE__       )       )

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=SCREAMING_SNAKE_CASE__       ,     d_kv=SCREAMING_SNAKE_CASE__       ,     num_heads=SCREAMING_SNAKE_CASE__       ,     dropout_rate=SCREAMING_SNAKE_CASE__       ,     layer_norm_epsilon=SCREAMING_SNAKE_CASE__       ,     )       )

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=SCREAMING_SNAKE_CASE__       ,     d_ff=SCREAMING_SNAKE_CASE__       ,     dropout_rate=SCREAMING_SNAKE_CASE__       ,     layer_norm_epsilon=SCREAMING_SNAKE_CASE__       )       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					Any=None       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     )				->	Any:
         lowerCAmelCase__          =			self.layer[0](
             SCREAMING_SNAKE_CASE__       ,     conditioning_emb=SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     )

         if encoder_hidden_states is not None:
              lowerCAmelCase__          =			torch.where(encoder_attention_mask > 0       ,     0       ,     -1e1_0       ).to(
                  encoder_hidden_states.dtype       )

              lowerCAmelCase__          =			self.layer[1](
                  SCREAMING_SNAKE_CASE__       ,     key_value_states=SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     )

         # Apply Film Conditional Feed Forward layer
         lowerCAmelCase__          =			self.layer[-1](SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         return (hidden_states,)






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Union[str, Any]:
         super().__init__()
         lowerCAmelCase__          =			TaLayerNorm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			TaFiLMLayer(in_features=d_model * 4       ,     out_features=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			Attention(query_dim=SCREAMING_SNAKE_CASE__       ,     heads=SCREAMING_SNAKE_CASE__       ,     dim_head=SCREAMING_SNAKE_CASE__       ,     out_bias=SCREAMING_SNAKE_CASE__       ,     scale_qk=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Dropout(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Any=None       ,     )				->	Optional[Any]:
         # pre_self_attention_layer_norm
         lowerCAmelCase__          =			self.layer_norm(SCREAMING_SNAKE_CASE__       )

         if conditioning_emb is not None:
              lowerCAmelCase__          =			self.FiLMLayer(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         # Self-attention block
         lowerCAmelCase__          =			self.attention(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			hidden_states + self.dropout(SCREAMING_SNAKE_CASE__       )

         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	int:
         super().__init__()
         lowerCAmelCase__          =			Attention(query_dim=SCREAMING_SNAKE_CASE__       ,     heads=SCREAMING_SNAKE_CASE__       ,     dim_head=SCREAMING_SNAKE_CASE__       ,     out_bias=SCREAMING_SNAKE_CASE__       ,     scale_qk=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			TaLayerNorm(SCREAMING_SNAKE_CASE__       ,     eps=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Dropout(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=None       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       ,     )				->	Tuple:
         lowerCAmelCase__          =			self.layer_norm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.attention(
             SCREAMING_SNAKE_CASE__       ,     encoder_hidden_states=SCREAMING_SNAKE_CASE__       ,     attention_mask=attention_mask.squeeze(1       )       ,     )
         lowerCAmelCase__          =			hidden_states + self.dropout(SCREAMING_SNAKE_CASE__       )
         return layer_output






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Union[str, Any]:
         super().__init__()
         lowerCAmelCase__          =			TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE__       ,     d_ff=SCREAMING_SNAKE_CASE__       ,     dropout_rate=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			TaFiLMLayer(in_features=d_model * 4       ,     out_features=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			TaLayerNorm(SCREAMING_SNAKE_CASE__       ,     eps=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Dropout(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=None       )				->	Tuple:
         lowerCAmelCase__          =			self.layer_norm(SCREAMING_SNAKE_CASE__       )
         if conditioning_emb is not None:
              lowerCAmelCase__          =			self.film(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.DenseReluDense(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			hidden_states + self.dropout(SCREAMING_SNAKE_CASE__       )
         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	Tuple:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     bias=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			nn.Dropout(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			NewGELUActivation()


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	int:
         lowerCAmelCase__          =			self.act(self.wi_a(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			self.wi_a(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			hidden_gelu * hidden_linear
         lowerCAmelCase__          =			self.dropout(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.wo(SCREAMING_SNAKE_CASE__       )
         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int=1e-6       )				->	Any:
         super().__init__()
         lowerCAmelCase__          =			nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			eps


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Union[str, Any]:
         # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
         # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
         # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
         # half-precision inputs is done in fp32

         lowerCAmelCase__          =			hidden_states.to(torch.floataa       ).pow(2       ).mean(-1       ,     keepdim=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			hidden_states * torch.rsqrt(variance + self.variance_epsilon       )

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
              lowerCAmelCase__          =			hidden_states.to(self.weight.dtype       )

         return self.weight * hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					torch.Tensor       )				->	torch.Tensor:
         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.044_715 * torch.pow(SCREAMING_SNAKE_CASE__       ,     3.0       ))       ))






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(SCREAMING_SNAKE_CASE__       ,     out_features * 2       ,     bias=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Any:
         lowerCAmelCase__          =			self.scale_bias(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__     ,		lowerCAmelCase__          =			torch.chunk(SCREAMING_SNAKE_CASE__       ,     2       ,     -1       )
         lowerCAmelCase__          =			x * (1 + scale) + shift
         return x






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

         lowerCAmelCase__          =			BlipImageProcessor()
         lowerCAmelCase__          =			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"       )

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta       )]

         lowerCAmelCase__          =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,     0       ,     -1       )       ) for x in image_inputs]

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         lowerCAmelCase__          =			self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"       )
         lowerCAmelCase__          =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         lowerCAmelCase__          =			BlipaProcessor.from_pretrained(
             self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE__       )

         self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )

         # test if it raises when no input is passed
         with pytest.raises(SCREAMING_SNAKE_CASE__       ):
              processor()


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )
