



"""simple docstring"""

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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Any       =			tempfile.mkdtemp()

     # fmt: off
     snake_case_				:  Union[str, 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
     snake_case_				:  Union[str, Any]       =			dict(zip(lowercase_						,  range(len(lowercase_       )       )       )       )
     snake_case_				:  str       =			['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
     snake_case_				:  Optional[int]       =			{'''unk_token''': '''<unk>'''}

     snake_case_				:  int       =			os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES['''vocab_file''']       )
     snake_case_				:  List[str]       =			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_       )       )

     snake_case_				:  Tuple       =			{
         '''do_resize''': True,
         '''size''': 20,
         '''do_center_crop''': True,
         '''crop_size''': 18,
         '''do_normalize''': True,
         '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
         '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
     }
     snake_case_				:  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 		_snake_case      (  self	: Optional[int]						,  **lowercase_	: Tuple       ):
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,  **lowercase_       )

 def 		_snake_case      (  self	: List[Any]						,  **lowercase_	: List[str]       ):
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,  **lowercase_       )

 def 		_snake_case      (  self	: Union[str, Any]						,  **lowercase_	: List[str]       ):
     return ViTImageProcessor.from_pretrained(self.tmpdirname						,  **lowercase_       )

 def 		_snake_case      (  self	: str       ):
     shutil.rmtree(self.tmpdirname       )

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  Tuple       =			[np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta       )]

     snake_case_				:  Union[str, Any]       =			[Image.fromarray(np.moveaxis(lowercase_						,  0						,  -1       )       ) for x in image_inputs]

     return image_inputs

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  int       =			self.get_tokenizer()
     snake_case_				:  Optional[int]       =			self.get_rust_tokenizer()
     snake_case_				:  Tuple       =			self.get_image_processor()

     snake_case_				:  Optional[int]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )
     processor_slow.save_pretrained(self.tmpdirname       )
     snake_case_				:  List[str]       =			CLIPSegProcessor.from_pretrained(self.tmpdirname						,  use_fast=lowercase_       )

     snake_case_				:  List[str]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )
     processor_fast.save_pretrained(self.tmpdirname       )
     snake_case_				:  Dict       =			CLIPSegProcessor.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 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Union[str, Any]       =			CLIPSegProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()       )
     processor.save_pretrained(self.tmpdirname       )

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

     snake_case_				:  Optional[int]       =			CLIPSegProcessor.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 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Any       =			self.get_image_processor()
     snake_case_				:  Optional[Any]       =			self.get_tokenizer()

     snake_case_				:  Any       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )

     snake_case_				:  Tuple       =			self.prepare_image_inputs()

     snake_case_				:  Any       =			image_processor(lowercase_						,  return_tensors='''np'''       )
     snake_case_				:  str       =			processor(images=lowercase_						,  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 		_snake_case      (  self	: Dict       ):
     snake_case_				:  List[str]       =			self.get_image_processor()
     snake_case_				:  List[str]       =			self.get_tokenizer()

     snake_case_				:  Optional[Any]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )

     snake_case_				:  Any       =			'''lower newer'''

     snake_case_				:  List[str]       =			processor(text=lowercase_       )

     snake_case_				:  Optional[int]       =			tokenizer(lowercase_       )

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

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  str       =			self.get_image_processor()
     snake_case_				:  List[str]       =			self.get_tokenizer()

     snake_case_				:  Union[str, Any]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )

     snake_case_				:  Any       =			'''lower newer'''
     snake_case_				:  Optional[int]       =			self.prepare_image_inputs()

     snake_case_				:  Optional[int]       =			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 		_snake_case      (  self	: str       ):
     snake_case_				:  Tuple       =			self.get_image_processor()
     snake_case_				:  Any       =			self.get_tokenizer()

     snake_case_				:  List[str]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )

     snake_case_				:  List[str]       =			self.prepare_image_inputs()
     snake_case_				:  List[Any]       =			self.prepare_image_inputs()

     snake_case_				:  Optional[int]       =			processor(images=lowercase_						,  visual_prompt=lowercase_       )

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

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






 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  str       =			self.get_image_processor()
     snake_case_				:  List[Any]       =			self.get_tokenizer()

     snake_case_				:  Optional[Any]       =			CLIPSegProcessor(tokenizer=lowercase_						,  image_processor=lowercase_       )

     snake_case_				:  List[str]       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_				:  Any       =			processor.batch_decode(lowercase_       )
     snake_case_				:  Tuple       =			tokenizer.batch_decode(lowercase_       )

     self.assertListEqual(lowercase_						,  lowercase_       )










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





