


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor




@require_vision
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=tempfile.mkdtemp()

             # fmt: off
             _lowerCamelCase       :		Optional[Any]		=['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
             # fmt: on
             _lowerCamelCase       :		Optional[int]		=dict(zip(lowercase_			,   range(len(lowercase_ ) ) ) )
             _lowerCamelCase       :		Tuple		=['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
             _lowerCamelCase       :		Optional[Any]		={'unk_token': '<unk>'}

             _lowerCamelCase       :		Optional[int]		=os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES['vocab_file'] )
             _lowerCamelCase       :		Any		=os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES['merges_file'] )
             with open(self.vocab_file			,   'w'			,   encoding='utf-8' ) as fp:
                    fp.write(json.dumps(lowercase_ ) + '\n' )
             with open(self.merges_file			,   'w'			,   encoding='utf-8' ) as fp:
                    fp.write('\n'.join(lowercase_ ) )

             _lowerCamelCase       :		List[str]		={
                 'do_resize': True,
                 'size': 20,
                 'do_center_crop': True,
                 'crop_size': 18,
                 'do_normalize': True,
                 'image_mean': [0.48145466, 0.4578275, 0.40821073],
                 'image_std': [0.26862954, 0.26130258, 0.27577711],
             }
             _lowerCamelCase       :		List[Any]		=os.path.join(self.tmpdirname			,   lowercase_ )
             with open(self.image_processor_file			,   'w'			,   encoding='utf-8' ) as fp:
                    json.dump(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Tuple			,   **lowercase_						:     str )					->						List[str]:
             """simple docstring"""




             return CLIPTokenizer.from_pretrained(self.tmpdirname			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     Tuple )					->						Tuple:
             """simple docstring"""




             return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     Dict			,   **lowercase_						:     List[Any] )					->						str:
             """simple docstring"""




             return CLIPImageProcessor.from_pretrained(self.tmpdirname			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						str:
             """simple docstring"""




             shutil.rmtree(self.tmpdirname )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=[np.random.randint(255			,   size=(3, 30, 400)			,   dtype=np.uinta )]

             _lowerCamelCase       :		Optional[int]		=[Image.fromarray(np.moveaxis(lowercase_			,   0			,   -1 ) ) for x in image_inputs]

             return image_inputs






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=self.get_tokenizer()
             _lowerCamelCase       :		str		=self.get_rust_tokenizer()
             _lowerCamelCase       :		Dict		=self.get_image_processor()

             _lowerCamelCase       :		List[Any]		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )
             processor_slow.save_pretrained(self.tmpdirname )
             _lowerCamelCase       :		Tuple		=CLIPProcessor.from_pretrained(self.tmpdirname			,   use_fast=lowercase_ )

             _lowerCamelCase       :		int		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )
             processor_fast.save_pretrained(self.tmpdirname )
             _lowerCamelCase       :		Union[str, Any]		=CLIPProcessor.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			,   lowercase_ )
             self.assertIsInstance(processor_fast.tokenizer			,   lowercase_ )

             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			,   lowercase_ )
             self.assertIsInstance(processor_fast.image_processor			,   lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=CLIPProcessor(tokenizer=self.get_tokenizer()			,   image_processor=self.get_image_processor() )
             processor.save_pretrained(self.tmpdirname )

             _lowerCamelCase       :		int		=self.get_tokenizer(bos_token='(BOS)'			,   eos_token='(EOS)' )
             _lowerCamelCase       :		List[str]		=self.get_image_processor(do_normalize=lowercase_			,   padding_value=1.0 )

             _lowerCamelCase       :		int		=CLIPProcessor.from_pretrained(
                 self.tmpdirname			,   bos_token='(BOS)'			,   eos_token='(EOS)'			,   do_normalize=lowercase_			,   padding_value=1.0 )

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

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






      def        lowerCamelCase       (					self						:     int )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.get_image_processor()
             _lowerCamelCase       :		int		=self.get_tokenizer()

             _lowerCamelCase       :		List[Any]		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )

             _lowerCamelCase       :		int		=self.prepare_image_inputs()

             _lowerCamelCase       :		str		=image_processor(lowercase_			,   return_tensors='np' )
             _lowerCamelCase       :		Dict		=processor(images=lowercase_			,   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        lowerCamelCase       (					self						:     Tuple )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.get_image_processor()
             _lowerCamelCase       :		Dict		=self.get_tokenizer()

             _lowerCamelCase       :		Any		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )

             _lowerCamelCase       :		Any		='lower newer'

             _lowerCamelCase       :		Dict		=processor(text=lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=tokenizer(lowercase_ )

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






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.get_image_processor()
             _lowerCamelCase       :		int		=self.get_tokenizer()

             _lowerCamelCase       :		Dict		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )

             _lowerCamelCase       :		List[Any]		='lower newer'
             _lowerCamelCase       :		List[Any]		=self.prepare_image_inputs()

             _lowerCamelCase       :		List[Any]		=processor(text=lowercase_			,   images=lowercase_ )

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

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






      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.get_image_processor()
             _lowerCamelCase       :		List[Any]		=self.get_tokenizer()

             _lowerCamelCase       :		Optional[int]		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )

             _lowerCamelCase       :		Any		=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase       :		Optional[int]		=processor.batch_decode(lowercase_ )
             _lowerCamelCase       :		List[str]		=tokenizer.batch_decode(lowercase_ )

             self.assertListEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     int )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.get_image_processor()
             _lowerCamelCase       :		Tuple		=self.get_tokenizer()

             _lowerCamelCase       :		Optional[Any]		=CLIPProcessor(tokenizer=lowercase_			,   image_processor=lowercase_ )

             _lowerCamelCase       :		Any		='lower newer'
             _lowerCamelCase       :		str		=self.prepare_image_inputs()

             _lowerCamelCase       :		Any		=processor(text=lowercase_			,   images=lowercase_ )

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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


