






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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								# 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=a   ) 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 				_UpperCamelCase     (			self   )			->       Optional[int]:
								# 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=a   ) as mock_head:
															snake_case_           =				GPTaTokenizerFast.from_pretrained('gpt2'   )
															# This check we did call the fake head request
															mock_head.assert_called()




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								# This test is for deprecated behavior and can be removed in v5
								try:
															snake_case_           =				tempfile.mktemp()
															with open(a			,     'wb'   ) as f:
																						http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'			,     a   )

															snake_case_           =				AlbertTokenizer.from_pretrained(a   )
								finally:
															os.remove(a   )

								# 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'			,     a   )
															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 				_UpperCamelCase     (			self   )			->       Any:
								# 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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




	@classmethod
	def 				_UpperCamelCase     (			cls   )			->       Dict:
								snake_case_           =				TOKEN
								HfFolder.save_token(a   )




	@classmethod
	def 				_UpperCamelCase     (			cls   )			->       Any:
								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 				_UpperCamelCase     (			self   )			->       List[str]:
								with tempfile.TemporaryDirectory() as tmp_dir:
															snake_case_           =				os.path.join(a			,     'vocab.txt'   )
															with open(a			,     'w'			,     encoding='utf-8'   ) as vocab_writer:
																						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )
															snake_case_           =				BertTokenizer(a   )

								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(a			,     repo_id='test-tokenizer'			,     push_to_hub=a			,     use_auth_token=self._token   )

								snake_case_           =				BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer'''   )
								self.assertDictEqual(new_tokenizer.vocab			,     tokenizer.vocab   )




	def 				_UpperCamelCase     (			self   )			->       Any:
								with tempfile.TemporaryDirectory() as tmp_dir:
															snake_case_           =				os.path.join(a			,     'vocab.txt'   )
															with open(a			,     'w'			,     encoding='utf-8'   ) as vocab_writer:
																						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )
															snake_case_           =				BertTokenizer(a   )

								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(
															    a			,     repo_id='valid_org/test-tokenizer-org'			,     push_to_hub=a			,     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 				_UpperCamelCase     (			self   )			->       List[Any]:
								CustomTokenizer.register_for_auto_class()
								with tempfile.TemporaryDirectory() as tmp_dir:
															snake_case_           =				os.path.join(a			,     'vocab.txt'   )
															with open(a			,     'w'			,     encoding='utf-8'   ) as vocab_writer:
																						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )
															snake_case_           =				CustomTokenizer(a   )

								# 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=a   )
								# 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(a			,     'vocab.txt'   )
															with open(a			,     'w'			,     encoding='utf-8'   ) as vocab_writer:
																						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]   )   )

															snake_case_           =				BertTokenizerFast.from_pretrained(a   )
															bert_tokenizer.save_pretrained(a   )
															snake_case_           =				CustomTokenizerFast.from_pretrained(a   )

								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=a   )
								# 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=a			,     trust_remote_code=a   )
								# 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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       int:
								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 				_UpperCamelCase     (			self   )			->       Optional[int]:
								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 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				Trie()
								trie.add('A'   )
								self.assertEqual(trie.split('ABC'   )			,     ['A', 'BC']   )
								self.assertEqual(trie.split('BCA'   )			,     ['BC', 'A']   )




	def 				_UpperCamelCase     (			self   )			->       Dict:
								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 				_UpperCamelCase     (			self   )			->       List[Any]:
								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 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				Trie()
								trie.add('AB'   )
								trie.add('B'   )
								trie.add('C'   )
								self.assertEqual(trie.split('ABC'   )			,     ['AB', 'C']   )




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								snake_case_           =				Trie()
								trie.add('ABC'   )
								trie.add('B'   )
								trie.add('CD'   )
								self.assertEqual(trie.split('ABCD'   )			,     ['ABC', 'D']   )






	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								# 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(a			,     ['AB', 'C']   )













from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





