

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       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						tempfile.mkdtemp()

      SCREAMING_SNAKE_CASE_									=						BlipImageProcessor()
      SCREAMING_SNAKE_CASE_									=						GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(_A						,		_A)

      processor.save_pretrained(self.tmpdirname)

  def 						lowerCAmelCase__     (					self						,		**_A):
      return AutoProcessor.from_pretrained(self.tmpdirname						,		**_A).tokenizer

  def 						lowerCAmelCase__     (					self						,		**_A):
      return AutoProcessor.from_pretrained(self.tmpdirname						,		**_A).image_processor

  def 						lowerCAmelCase__     (					self):
      shutil.rmtree(self.tmpdirname)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						[np.random.randint(255						,		size=(3, 30, 400)						,		dtype=np.uinta)]

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

      return image_inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=self.get_tokenizer()						,		image_processor=self.get_image_processor())
      processor.save_pretrained(self.tmpdirname)

      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer(bos_token='(BOS)'						,		eos_token='(EOS)')
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor(do_normalize=_A						,		padding_value=1.0)

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor.from_pretrained(
          self.tmpdirname						,		bos_token='(BOS)'						,		eos_token='(EOS)'						,		do_normalize=_A						,		padding_value=1.0)

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

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=_A						,		image_processor=_A)

      SCREAMING_SNAKE_CASE_									=						self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processor(_A						,		return_tensors='np')
      SCREAMING_SNAKE_CASE_									=						processor(images=_A						,		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 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=_A						,		image_processor=_A)

      SCREAMING_SNAKE_CASE_									=						'lower newer'

      SCREAMING_SNAKE_CASE_									=						processor(text=_A)

      SCREAMING_SNAKE_CASE_									=						tokenizer(_A						,		return_token_type_ids=_A)

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=_A						,		image_processor=_A)

      SCREAMING_SNAKE_CASE_									=						'lower newer'
      SCREAMING_SNAKE_CASE_									=						self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_									=						processor(text=_A						,		images=_A)

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

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=_A						,		image_processor=_A)

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

      SCREAMING_SNAKE_CASE_									=						processor.batch_decode(_A)
      SCREAMING_SNAKE_CASE_									=						tokenizer.batch_decode(_A)

      self.assertListEqual(_A						,		_A)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()

      SCREAMING_SNAKE_CASE_									=						BlipaProcessor(tokenizer=_A						,		image_processor=_A)

      SCREAMING_SNAKE_CASE_									=						'lower newer'
      SCREAMING_SNAKE_CASE_									=						self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_									=						processor(text=_A						,		images=_A)

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





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


