import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 					A							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Optional[Any]              =      CLIPTokenizer
   __UpperCAmelCase			:			Union[str, Any]              =      CLIPTokenizerFast
   __UpperCAmelCase			:			Any              =      True
   __UpperCAmelCase			:			Union[str, Any]              =      {}
   __UpperCAmelCase			:			str              =      False


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



         super().setUp()

         # fmt: off
         UpperCAmelCase__								=  ["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
         UpperCAmelCase__								=  dict(zip(__UpperCAmelCase	,  range(len(__UpperCAmelCase		)		)		)		)
         UpperCAmelCase__								=  ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
         UpperCAmelCase__								=  {"unk_token": "<unk>"}

         UpperCAmelCase__								=  os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  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(__UpperCAmelCase		) + "\n"		)
         with open(self.merges_file	,  "w"	,  encoding="utf-8"		) as fp:
               fp.write("\n".join(__UpperCAmelCase		)		)


   def       lowercase_				(self					:						int	,  **__UpperCAmelCase					:						Optional[int]		)						->    Dict:
         """simple docstring"""



         kwargs.update(self.special_tokens_map		)
         return CLIPTokenizer.from_pretrained(self.tmpdirname	,  **__UpperCAmelCase		)


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



         kwargs.update(self.special_tokens_map		)
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "lower newer"
         UpperCAmelCase__								=  "lower newer"
         return input_text, output_text


   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  CLIPTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map		)
         UpperCAmelCase__								=  "lower newer"
         UpperCAmelCase__								=  ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
         UpperCAmelCase__								=  tokenizer.tokenize(__UpperCAmelCase		)
         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  tokens + [tokenizer.unk_token]
         UpperCAmelCase__								=  [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase		)	,  __UpperCAmelCase		)


   @require_ftfy
   def       lowercase_				(self					:						int		)						->    List[str]:
         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
                     UpperCAmelCase__								=  self.tokenizer_class.from_pretrained(__UpperCAmelCase	,  **__UpperCAmelCase		)
                     UpperCAmelCase__								=  self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase	,  **__UpperCAmelCase		)

                     UpperCAmelCase__								=  "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
                     UpperCAmelCase__								=  tokenizer_s.tokenize(__UpperCAmelCase		)
                     UpperCAmelCase__								=  tokenizer_r.tokenize(__UpperCAmelCase		)

                     self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

                     # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                     # with Tilde) encoded in 2 different ways
                     UpperCAmelCase__								=  "xa\u0303y" + " " + "x\xe3y"
                     UpperCAmelCase__								=  tokenizer_s.tokenize(__UpperCAmelCase		)
                     UpperCAmelCase__								=  tokenizer_r.tokenize(__UpperCAmelCase		)

                     self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

                     # Test that the tokenization is identical on unicode of space type
                     UpperCAmelCase__								=  [
                         "\u0009",  # (horizontal tab, '\t')
                         "\u000B",  # (vertical tab)
                         "\u000C",  # (form feed)
                         "\u0020",  # (space, ' ')
                         "\u200E",  # (left-to-right mark):w
                         "\u200F",  # (right-to-left mark)
                     ]
                     for unicode_seq in spaces_unicodes:
                           UpperCAmelCase__								=  tokenizer_s.tokenize(__UpperCAmelCase		)
                           UpperCAmelCase__								=  tokenizer_r.tokenize(__UpperCAmelCase		)

                           self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

                     # Test that the tokenization is identical on unicode of line break type
                     UpperCAmelCase__								=  [
                         "\u000A",  # (line feed, '\n')
                         "\r\n",  # (carriage return and line feed, '\r\n')
                         "\u000D",  # (carriage return, '\r')
                         "\r",  # (carriage return, '\r')
                         "\u000D",  # (carriage return, '\r')
                         "\u2028",  # (line separator)
                         "\u2029",  # (paragraph separator)
                         # "\u0085", # (next line)
                     ]

                     # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                     # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                     # space (and thus into an empty list).

                     for unicode_seq in line_break_unicodes:
                           UpperCAmelCase__								=  tokenizer_s.tokenize(__UpperCAmelCase		)
                           UpperCAmelCase__								=  tokenizer_r.tokenize(__UpperCAmelCase		)

                           self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple		)						->    str:
         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
                     UpperCAmelCase__								=  "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                     UpperCAmelCase__								=  f"""{text_of_1_token} {text_of_1_token}"""

                     UpperCAmelCase__								=  self.rust_tokenizer_class.from_pretrained(
                         __UpperCAmelCase	,  use_fast=__UpperCAmelCase	,  )
                     UpperCAmelCase__								=  tokenizer_r(__UpperCAmelCase	,  return_offsets_mapping=__UpperCAmelCase	,  add_special_tokens=__UpperCAmelCase		)
                     self.assertEqual(encoding.offset_mapping[0]	,  (0, len(__UpperCAmelCase		))		)
                     self.assertEqual(
                         encoding.offset_mapping[1]	,  (len(__UpperCAmelCase		) + 1, len(__UpperCAmelCase		) + 1 + len(__UpperCAmelCase		))	,  )

                     UpperCAmelCase__								=  f""" {text}"""

                     UpperCAmelCase__								=  self.rust_tokenizer_class.from_pretrained(
                         __UpperCAmelCase	,  use_fast=__UpperCAmelCase	,  )
                     UpperCAmelCase__								=  tokenizer_r(__UpperCAmelCase	,  return_offsets_mapping=__UpperCAmelCase	,  add_special_tokens=__UpperCAmelCase		)
                     self.assertEqual(encoding.offset_mapping[0]	,  (1, 1 + len(__UpperCAmelCase		))		)
                     self.assertEqual(
                         encoding.offset_mapping[1]	,  (1 + len(__UpperCAmelCase		) + 1, 1 + len(__UpperCAmelCase		) + 1 + len(__UpperCAmelCase		))	,  )


   def       lowercase_				(self					:						str		)						->    int:
         """simple docstring"""



         with self.assertRaises(__UpperCAmelCase		) as context:
               self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"		)

         self.assertTrue(
             context.exception.args[0].startswith(
                 "The `backend_tokenizer` provided does not match the expected format."		)		)


   @require_ftfy
   def       lowercase_				(self					:						Optional[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         super().test_tokenization_python_rust_equals()





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



         pass




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,
      )



