
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
     from test_module.custom_tokenization_fast import CustomTokenizerFast




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          # A mock response for an HTTP head request to emulate server down
          snake_case__						=				mock.Mock()
          snake_case__						=				5_00
          snake_case__						=				{}
          snake_case__						=				HTTPError
          snake_case__						=				{}

          # Download this model to make sure it's in the cache.
          snake_case__						=				BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch("requests.Session.request"		,	return_value=lowerCamelCase ) as mock_head:
              snake_case__						=				BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
              # This check we did call the fake head request
              mock_head.assert_called()
      @require_tokenizers
      def A_			(			self ):
          # A mock response for an HTTP head request to emulate server down
          snake_case__						=				mock.Mock()
          snake_case__						=				5_00
          snake_case__						=				{}
          snake_case__						=				HTTPError
          snake_case__						=				{}

          # Download this model to make sure it's in the cache.
          snake_case__						=				GPTaTokenizerFast.from_pretrained("gpt2" )

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch("requests.Session.request"		,	return_value=lowerCamelCase ) as mock_head:
              snake_case__						=				GPTaTokenizerFast.from_pretrained("gpt2" )
              # This check we did call the fake head request
              mock_head.assert_called()
      def A_			(			self ):
          # This test is for deprecated behavior and can be removed in v5
          try:
              snake_case__						=				tempfile.mktemp()
              with open(lowerCamelCase		,	"wb" ) as f:
                  http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"		,	lowerCamelCase )

              snake_case__						=				AlbertTokenizer.from_pretrained(lowerCamelCase )
          finally:
              os.remove(lowerCamelCase )

          # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
          # the current folder and have the right name.
          if os.path.isfile("tokenizer.json" ):
              # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
              return
          try:
              with open("tokenizer.json"		,	"wb" ) as f:
                  http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"		,	lowerCamelCase )
              snake_case__						=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
              # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
              self.assertEqual(tokenizer.vocab_size		,	10_00 )
              # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

          finally:
              os.remove("tokenizer.json" )



      def A_			(			self ):
          # This test is for deprecated behavior and can be removed in v5
          snake_case__						=				AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )





@is_staging_test
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		Any    	=     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
      @classmethod
      def A_			(			cls ):
          snake_case__						=				TOKEN
          HfFolder.save_token(lowerCamelCase )
      @classmethod
      def A_			(			cls ):
          try:
              delete_repo(token=cls._token		,	repo_id="test-tokenizer" )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token		,	repo_id="valid_org/test-tokenizer-org" )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token		,	repo_id="test-dynamic-tokenizer" )
          except HTTPError:
              pass
      def A_			(			self ):
          with tempfile.TemporaryDirectory() as tmp_dir:
              snake_case__						=				os.path.join(lowerCamelCase		,	"vocab.txt" )
              with open(lowerCamelCase		,	"w"		,	encoding="utf-8" ) as vocab_writer:
                  vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
              snake_case__						=				BertTokenizer(lowerCamelCase )

          tokenizer.push_to_hub("test-tokenizer"		,	use_auth_token=self._token )
          snake_case__						=				BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""" )
          self.assertDictEqual(new_tokenizer.vocab		,	tokenizer.vocab )

          # Reset repo
          delete_repo(token=self._token		,	repo_id="test-tokenizer" )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(lowerCamelCase		,	repo_id="test-tokenizer"		,	push_to_hub=lowerCamelCase		,	use_auth_token=self._token )

          snake_case__						=				BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer""" )
          self.assertDictEqual(new_tokenizer.vocab		,	tokenizer.vocab )
      def A_			(			self ):
          with tempfile.TemporaryDirectory() as tmp_dir:
              snake_case__						=				os.path.join(lowerCamelCase		,	"vocab.txt" )
              with open(lowerCamelCase		,	"w"		,	encoding="utf-8" ) as vocab_writer:
                  vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
              snake_case__						=				BertTokenizer(lowerCamelCase )

          tokenizer.push_to_hub("valid_org/test-tokenizer-org"		,	use_auth_token=self._token )
          snake_case__						=				BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
          self.assertDictEqual(new_tokenizer.vocab		,	tokenizer.vocab )

          # Reset repo
          delete_repo(token=self._token		,	repo_id="valid_org/test-tokenizer-org" )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(
                  lowerCamelCase		,	repo_id="valid_org/test-tokenizer-org"		,	push_to_hub=lowerCamelCase		,	use_auth_token=self._token )

          snake_case__						=				BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
          self.assertDictEqual(new_tokenizer.vocab		,	tokenizer.vocab )



      @require_tokenizers
      def A_			(			self ):
          CustomTokenizer.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              snake_case__						=				os.path.join(lowerCamelCase		,	"vocab.txt" )
              with open(lowerCamelCase		,	"w"		,	encoding="utf-8" ) as vocab_writer:
                  vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
              snake_case__						=				CustomTokenizer(lowerCamelCase )

          # No fast custom tokenizer
          tokenizer.push_to_hub("test-dynamic-tokenizer"		,	use_auth_token=self._token )

          snake_case__						=				AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""		,	trust_remote_code=lowerCamelCase )
          # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__		,	"CustomTokenizer" )

          # Fast and slow custom tokenizer
          CustomTokenizerFast.register_for_auto_class()
          with tempfile.TemporaryDirectory() as tmp_dir:
              snake_case__						=				os.path.join(lowerCamelCase		,	"vocab.txt" )
              with open(lowerCamelCase		,	"w"		,	encoding="utf-8" ) as vocab_writer:
                  vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )

              snake_case__						=				BertTokenizerFast.from_pretrained(lowerCamelCase )
              bert_tokenizer.save_pretrained(lowerCamelCase )
              snake_case__						=				CustomTokenizerFast.from_pretrained(lowerCamelCase )

          tokenizer.push_to_hub("test-dynamic-tokenizer"		,	use_auth_token=self._token )

          snake_case__						=				AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""		,	trust_remote_code=lowerCamelCase )
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__		,	"CustomTokenizerFast" )
          snake_case__						=				AutoTokenizer.from_pretrained(
              F"""{USER}/test-dynamic-tokenizer"""		,	use_fast=lowerCamelCase		,	trust_remote_code=lowerCamelCase )
          # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
          self.assertEqual(tokenizer.__class__.__name__		,	"CustomTokenizer" )





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("Hello 友達" )
          self.assertEqual(trie.data		,	{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
          trie.add("Hello" )
          trie.data
          self.assertEqual(trie.data		,	{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
      def A_			(			self ):
          snake_case__						=				Trie()
          self.assertEqual(trie.split("[CLS] This is a extra_id_100" )		,	["[CLS] This is a extra_id_100"] )
          trie.add("[CLS]" )
          trie.add("extra_id_1" )
          trie.add("extra_id_100" )
          self.assertEqual(trie.split("[CLS] This is a extra_id_100" )		,	["[CLS]", " This is a ", "extra_id_100"] )
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("A" )
          self.assertEqual(trie.split("ABC" )		,	["A", "BC"] )
          self.assertEqual(trie.split("BCA" )		,	["BC", "A"] )
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("TOKEN]" )
          trie.add("[SPECIAL_TOKEN]" )
          self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" )		,	["This is something ", "[SPECIAL_TOKEN]"] )
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("A" )
          trie.add("P" )
          trie.add("[SPECIAL_TOKEN]" )
          self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" )		,	["This is something ", "[SPECIAL_TOKEN]"] )
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("AB" )
          trie.add("B" )
          trie.add("C" )
          self.assertEqual(trie.split("ABC" )		,	["AB", "C"] )
      def A_			(			self ):
          snake_case__						=				Trie()
          trie.add("ABC" )
          trie.add("B" )
          trie.add("CD" )
          self.assertEqual(trie.split("ABCD" )		,	["ABC", "D"] )



      def A_			(			self ):
          # Even if the offsets are wrong, we necessarily output correct string
          # parts.
          snake_case__						=				Trie()
          snake_case__						=				trie.cut_text("ABC"		,	[0, 0, 2, 1, 2, 3] )
          self.assertEqual(lowerCamelCase		,	["AB", "C"] )





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



