




"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	List[str]				)				->							Dict:




     '''simple docstring'''

     A__	=    tempfile.mkdtemp()

     A__	=    [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     A__	=    os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES["vocab_file"]				)
     with open(self.vocab_file     ,			"w"     ,			encoding="utf-8"				) as vocab_writer:
      vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

     A__	=    {
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
         "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
     }
     A__	=    os.path.join(self.tmpdirname     ,			snake_case_				)
     with open(self.image_processor_file     ,			"w"     ,			encoding="utf-8"				) as fp:
      json.dump(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			**snake_case_						:	List[Any]				)				->							str:




     '''simple docstring'''

     return BertTokenizer.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			**snake_case_						:	Tuple				)				->							Any:




     '''simple docstring'''

     return BertTokenizerFast.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    [np.random.randint(255     ,			size=(3, 30, 400)     ,			dtype=np.uinta				)]
     A__	=    [Image.fromarray(np.moveaxis(snake_case_     ,			0     ,			-1				)				) for x in image_inputs]
     return image_inputs




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()
     A__	=    self.get_rust_tokenizer()
     A__	=    self.get_image_processor()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_slow.save_pretrained(self.tmpdirname				)
     A__	=    AlignProcessor.from_pretrained(self.tmpdirname     ,			use_fast=snake_case_				)

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_fast.save_pretrained(self.tmpdirname				)
     A__	=    AlignProcessor.from_pretrained(self.tmpdirname				)

     self.assertEqual(processor_slow.tokenizer.get_vocab()     ,			tokenizer_slow.get_vocab()				)
     self.assertEqual(processor_fast.tokenizer.get_vocab()     ,			tokenizer_fast.get_vocab()				)
     self.assertEqual(tokenizer_slow.get_vocab()     ,			tokenizer_fast.get_vocab()				)
     self.assertIsInstance(processor_slow.tokenizer     ,			snake_case_				)
     self.assertIsInstance(processor_fast.tokenizer     ,			snake_case_				)

     self.assertEqual(processor_slow.image_processor.to_json_string()     ,			image_processor.to_json_string()				)
     self.assertEqual(processor_fast.image_processor.to_json_string()     ,			image_processor.to_json_string()				)
     self.assertIsInstance(processor_slow.image_processor     ,			snake_case_				)
     self.assertIsInstance(processor_fast.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    AlignProcessor(tokenizer=self.get_tokenizer()     ,			image_processor=self.get_image_processor()				)
     processor.save_pretrained(self.tmpdirname				)

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

     A__	=    AlignProcessor.from_pretrained(
         self.tmpdirname     ,			bos_token="(BOS)"     ,			eos_token="(EOS)"     ,			do_normalize=snake_case_     ,			padding_value=1.0				)

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

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




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    self.prepare_image_inputs()

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=snake_case_     ,			return_tensors="np"				)

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




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    "lower newer"

     A__	=    processor(text=snake_case_				)

     A__	=    tokenizer(snake_case_     ,			padding="max_length"     ,			max_length=64				)

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




    def 							__magic_name__							(   self						:	str				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

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

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

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

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




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

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

     A__	=    processor.batch_decode(snake_case_				)
     A__	=    tokenizer.batch_decode(snake_case_				)

     self.assertListEqual(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    AlignProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

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

     A__	=    processor(text=snake_case_     ,			images=snake_case_				)

     self.assertListEqual(list(inputs.keys()				)     ,			processor.model_input_names				)











"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      UpperCAmelCase_  (						nn.Module							):




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	int = 16     ,			snake_case_						:	int = 88     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	int = 1     ,			snake_case_						:	float = 0.0     ,			snake_case_						:	int = 32     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	bool = False     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "geglu"     ,			snake_case_						:	Optional[int] = None     ,			)				->							str:




     '''simple docstring'''

     super().__init__()
     A__	=    nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=snake_case_     ,			attention_head_dim=snake_case_     ,			in_channels=snake_case_     ,			num_layers=snake_case_     ,			dropout=snake_case_     ,			norm_num_groups=snake_case_     ,			cross_attention_dim=snake_case_     ,			attention_bias=snake_case_     ,			sample_size=snake_case_     ,			num_vector_embeds=snake_case_     ,			activation_fn=snake_case_     ,			num_embeds_ada_norm=snake_case_     ,			)
             for _ in range(2				)
         ]				)

     # Variables that can be set by a pipeline:

     # The ratio of transformer1 to transformer2's output states to be combined during inference
     A__	=    0.5

     # The shape of `encoder_hidden_states` is expected to be
     # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
     A__	=    [77, 257]

     # Which transformer to use to encode which condition.
     # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
     A__	=    [1, 0]




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	bool = True     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    hidden_states

     A__	=    []
     A__	=    0
     # attention_mask is not used yet
     for i in range(2				):
      # for each of the two transformers, pass the corresponding condition tokens
      A__	=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      A__	=    self.transformer_index_for_condition[i]
      A__	=    self.transformers[transformer_index](
          snake_case_     ,			encoder_hidden_states=snake_case_     ,			timestep=snake_case_     ,			cross_attention_kwargs=snake_case_     ,			return_dict=snake_case_     ,			)[0]
      encoded_states.append(encoded_state - input_states				)
      tokens_start += self.condition_lengths[i]

     A__	=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
     A__	=    output_states + input_states

     if not return_dict:
      return (output_states,)

     return TransformeraDModelOutput(sample=snake_case_				)





