
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 						lowercase__  (						unittest.TestCase  ):
	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= tempfile.mkdtemp()

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

						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE__													= 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_							)							)

						SCREAMING_SNAKE_CASE__													= {
						    "do_resize": True,
						    "size": 20,
						    "do_center_crop": True,
						    "crop_size": 18,
						    "do_normalize": True,
						    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
						    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
						}
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   UpperCAmelCase_							)
						with open(self.image_processor_file       ,   'w'       ,   encoding='utf-8'							) as fp:
											json.dump(UpperCAmelCase_       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Dict							):
						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   **UpperCAmelCase_							:   List[Any]							):
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						shutil.rmtree(self.tmpdirname							)
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta							)]

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

						return image_inputs
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)
						processor_slow.save_pretrained(self.tmpdirname							)
						SCREAMING_SNAKE_CASE__													= CLIPProcessor.from_pretrained(self.tmpdirname       ,   use_fast=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)
						processor_fast.save_pretrained(self.tmpdirname							)
						SCREAMING_SNAKE_CASE__													= 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       ,   UpperCAmelCase_							)
						self.assertIsInstance(processor_fast.tokenizer       ,   UpperCAmelCase_							)

						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       ,   UpperCAmelCase_							)
						self.assertIsInstance(processor_fast.image_processor       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= CLIPProcessor(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=UpperCAmelCase_       ,   padding_value=1.0							)

						SCREAMING_SNAKE_CASE__													= CLIPProcessor.from_pretrained(
						    self.tmpdirname       ,   bos_token='(BOS)'       ,   eos_token='(EOS)'       ,   do_normalize=UpperCAmelCase_       ,   padding_value=1.0							)

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

						self.assertEqual(processor.image_processor.to_json_string()       ,   image_processor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.image_processor       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__													= image_processor(UpperCAmelCase_       ,   return_tensors='np'							)
						SCREAMING_SNAKE_CASE__													= processor(images=UpperCAmelCase_       ,   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      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= "lower newer"

						SCREAMING_SNAKE_CASE__													= processor(text=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer(UpperCAmelCase_							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]							)
	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= processor(text=UpperCAmelCase_       ,   images=UpperCAmelCase_							)

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

						# test if it raises when no input is passed
						with pytest.raises(UpperCAmelCase_							):
											processor()
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= processor.batch_decode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= tokenizer.batch_decode(UpperCAmelCase_							)

						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.get_image_processor()
						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__													= CLIPProcessor(tokenizer=UpperCAmelCase_       ,   image_processor=UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= processor(text=UpperCAmelCase_       ,   images=UpperCAmelCase_							)

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

