
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
						from test_module.custom_tokenization_fast import CustomTokenizerFast


class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 0




		@slow
		def     lowercase       (				self      ):
								for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
														self.assertIsNotNone(UpperCAmelCase_      )
														self.assertIsInstance(UpperCAmelCase_					,						(BertTokenizer, BertTokenizerFast)      )
														self.assertGreater(len(UpperCAmelCase_      )					,						0      )

								for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
														self.assertIsNotNone(UpperCAmelCase_      )
														self.assertIsInstance(UpperCAmelCase_					,						(GPTaTokenizer, GPTaTokenizerFast)      )
														self.assertGreater(len(UpperCAmelCase_      )					,						0      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
								self.assertIsInstance(UpperCAmelCase_					,						(BertTokenizer, BertTokenizerFast)      )
								self.assertEqual(tokenizer.vocab_size					,						12      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
								self.assertIsInstance(UpperCAmelCase_					,						(RobertaTokenizer, RobertaTokenizerFast)      )
								self.assertEqual(tokenizer.vocab_size					,						20      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoConfig.from_pretrained(UpperCAmelCase_      )
								self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )
								# Check that tokenizer_type ≠ model_type
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						config=UpperCAmelCase_      )
								self.assertIsInstance(UpperCAmelCase_					,						(BertTokenizer, BertTokenizerFast)      )
								self.assertEqual(tokenizer.vocab_size					,						12      )




		def     lowercase       (				self      ):
								with tempfile.TemporaryDirectory() as tmp_dir:
														shutil.copy("./tests/fixtures/vocab.txt"					,						os.path.join(UpperCAmelCase_					,						"vocab.txt"      )      )

														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						tokenizer_type="bert"					,						use_fast=UpperCAmelCase_      )
														self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )

								with tempfile.TemporaryDirectory() as tmp_dir:
														shutil.copy("./tests/fixtures/vocab.json"					,						os.path.join(UpperCAmelCase_					,						"vocab.json"      )      )
														shutil.copy("./tests/fixtures/merges.txt"					,						os.path.join(UpperCAmelCase_					,						"merges.txt"      )      )

														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						tokenizer_type="gpt2"					,						use_fast=UpperCAmelCase_      )
														self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )




		@require_tokenizers
		def     lowercase       (				self      ):
								with tempfile.TemporaryDirectory() as tmp_dir:
														shutil.copy("./tests/fixtures/vocab.txt"					,						os.path.join(UpperCAmelCase_					,						"vocab.txt"      )      )

														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						tokenizer_type="bert"      )
														self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )

								with tempfile.TemporaryDirectory() as tmp_dir:
														shutil.copy("./tests/fixtures/vocab.json"					,						os.path.join(UpperCAmelCase_					,						"vocab.json"      )      )
														shutil.copy("./tests/fixtures/merges.txt"					,						os.path.join(UpperCAmelCase_					,						"merges.txt"      )      )

														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						tokenizer_type="gpt2"      )
														self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )




		def     lowercase       (				self      ):
								with pytest.raises(UpperCAmelCase_      ):
														AutoTokenizer.from_pretrained("./"					,						tokenizer_type="xxx"      )




		@require_tokenizers
		def     lowercase       (				self      ):
								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
														_SCREAMING_SNAKE_CASE				= tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased"      )
														self.assertIsInstance(UpperCAmelCase_					,						(BertTokenizer, BertTokenizerFast)      )

														if isinstance(UpperCAmelCase_					,						UpperCAmelCase_      ):
																				self.assertEqual(tokenizer.basic_tokenizer.do_lower_case					,						UpperCAmelCase_      )
														else:
																				self.assertEqual(tokenizer.do_lower_case					,						UpperCAmelCase_      )

														self.assertEqual(tokenizer.model_max_length					,						512      )




		@require_tokenizers
		def     lowercase       (				self      ):
								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
														with self.assertRaisesRegex(
														    UpperCAmelCase_					,						"julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"					,						):
																				_SCREAMING_SNAKE_CASE				= tokenizer_class.from_pretrained("julien-c/herlolip-not-exists"      )




		def     lowercase       (				self      ):
								# tests: https://github.com/huggingface/transformers/pull/13251
								# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
								# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
								_SCREAMING_SNAKE_CASE				= TOKENIZER_MAPPING.values()
								_SCREAMING_SNAKE_CASE				= []

								for slow_tok, fast_tok in tokenizers:
														if slow_tok is not None:
																				tokenizer_names.append(slow_tok.__name__      )

														if fast_tok is not None:
																				tokenizer_names.append(fast_tok.__name__      )

								for tokenizer_name in tokenizer_names:
														# must find the right class
														tokenizer_class_from_name(UpperCAmelCase_      )




		@require_tokenizers
		def     lowercase       (				self      ):
								self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"					,						use_fast=UpperCAmelCase_      )					,						UpperCAmelCase_      )
								self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"      )					,						UpperCAmelCase_      )




		@require_tokenizers
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("distilbert-base-uncased"					,						do_lower_case=UpperCAmelCase_      )
								_SCREAMING_SNAKE_CASE				= "Hello, world. How are you?"
								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize(UpperCAmelCase_      )
								self.assertEqual("[UNK]"					,						tokens[0]      )

								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("microsoft/mpnet-base"					,						do_lower_case=UpperCAmelCase_      )
								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize(UpperCAmelCase_      )
								self.assertEqual("[UNK]"					,						tokens[0]      )




		@require_tokenizers
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config"      )
								self.assertEqual(type(UpperCAmelCase_      )					,						UpperCAmelCase_      )
								self.assertEqual(tokenizer.model_max_length					,						512      )
								self.assertEqual(tokenizer.vocab_size					,						30_000      )
								self.assertEqual(tokenizer.unk_token					,						"[UNK]"      )
								self.assertEqual(tokenizer.padding_side					,						"right"      )
								self.assertEqual(tokenizer.truncation_side					,						"right"      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
								self.assertIsInstance(UpperCAmelCase_					,						(BertTokenizer, BertTokenizerFast)      )
								with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer.save_pretrained(UpperCAmelCase_      )
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )

								self.assertIsInstance(UpperCAmelCase_					,						tokenizer.__class__      )
								self.assertEqual(tokenizera.vocab_size					,						12      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("ctrl"      )
								# There is no fast CTRL so this always gives us a slow tokenizer.
								self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )




		def     lowercase       (				self      ):
								# Check we can load the tokenizer config of an online model.
								_SCREAMING_SNAKE_CASE				= get_tokenizer_config("bert-base-cased"      )
								_SCREAMING_SNAKE_CASE				= config.pop("_commit_hash"					,						UpperCAmelCase_      )
								# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
								self.assertEqual(UpperCAmelCase_					,						{"do_lower_case": False}      )

								# This model does not have a tokenizer_config so we get back an empty dict.
								_SCREAMING_SNAKE_CASE				= get_tokenizer_config(UpperCAmelCase_      )
								self.assertDictEqual(UpperCAmelCase_					,						{}      )

								# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
								with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer.save_pretrained(UpperCAmelCase_      )
														_SCREAMING_SNAKE_CASE				= get_tokenizer_config(UpperCAmelCase_      )

								# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
								self.assertEqual(config["tokenizer_class"]					,						"BertTokenizer"      )




		def     lowercase       (				self      ):
								try:
														AutoConfig.register("custom"					,						UpperCAmelCase_      )

														AutoTokenizer.register(UpperCAmelCase_					,						slow_tokenizer_class=UpperCAmelCase_      )
														# Trying to register something existing in the Transformers library will raise an error
														with self.assertRaises(UpperCAmelCase_      ):
																				AutoTokenizer.register(UpperCAmelCase_					,						slow_tokenizer_class=UpperCAmelCase_      )

														_SCREAMING_SNAKE_CASE				= CustomTokenizer.from_pretrained(UpperCAmelCase_      )
														with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(UpperCAmelCase_      )

																				_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
																				self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )

								finally:
														if "custom" in CONFIG_MAPPING._extra_content:
																				del CONFIG_MAPPING._extra_content["custom"]
														if CustomConfig in TOKENIZER_MAPPING._extra_content:
																				del TOKENIZER_MAPPING._extra_content[CustomConfig]




		@require_tokenizers
		def     lowercase       (				self      ):
								try:
														AutoConfig.register("custom"					,						UpperCAmelCase_      )

														# Can register in two steps
														AutoTokenizer.register(UpperCAmelCase_					,						slow_tokenizer_class=UpperCAmelCase_      )
														self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,						(CustomTokenizer, None)      )
														AutoTokenizer.register(UpperCAmelCase_					,						fast_tokenizer_class=UpperCAmelCase_      )
														self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,						(CustomTokenizer, CustomTokenizerFast)      )

														del TOKENIZER_MAPPING._extra_content[CustomConfig]
														# Can register in one step
														AutoTokenizer.register(
														    UpperCAmelCase_					,						slow_tokenizer_class=UpperCAmelCase_					,						fast_tokenizer_class=UpperCAmelCase_      )
														self.assertEqual(TOKENIZER_MAPPING[CustomConfig]					,						(CustomTokenizer, CustomTokenizerFast)      )

														# Trying to register something existing in the Transformers library will raise an error
														with self.assertRaises(UpperCAmelCase_      ):
																				AutoTokenizer.register(UpperCAmelCase_					,						fast_tokenizer_class=UpperCAmelCase_      )

														# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
														# and that model does not have a tokenizer.json
														with tempfile.TemporaryDirectory() as tmp_dir:
																				_SCREAMING_SNAKE_CASE				= BertTokenizerFast.from_pretrained(UpperCAmelCase_      )
																				bert_tokenizer.save_pretrained(UpperCAmelCase_      )
																				_SCREAMING_SNAKE_CASE				= CustomTokenizerFast.from_pretrained(UpperCAmelCase_      )

														with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(UpperCAmelCase_      )

																				_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_      )
																				self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )

																				_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
																				self.assertIsInstance(UpperCAmelCase_					,						UpperCAmelCase_      )

								finally:
														if "custom" in CONFIG_MAPPING._extra_content:
																				del CONFIG_MAPPING._extra_content["custom"]
														if CustomConfig in TOKENIZER_MAPPING._extra_content:
																				del TOKENIZER_MAPPING._extra_content[CustomConfig]




		def     lowercase       (				self      ):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(UpperCAmelCase_      ):
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"      )
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(UpperCAmelCase_      ):
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_      )

								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_      )
								self.assertTrue(tokenizer.special_attribute_present      )
								# Test tokenizer can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
														tokenizer.save_pretrained(UpperCAmelCase_      )
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						trust_remote_code=UpperCAmelCase_      )
								self.assertTrue(reloaded_tokenizer.special_attribute_present      )

								if is_tokenizers_available():
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizerFast"      )
														self.assertEqual(reloaded_tokenizer.__class__.__name__					,						"NewTokenizerFast"      )

														# Test we can also load the slow version
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
														self.assertTrue(tokenizer.special_attribute_present      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
														# Test tokenizer can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																				tokenizer.save_pretrained(UpperCAmelCase_      )
																				_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						trust_remote_code=UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
														self.assertEqual(reloaded_tokenizer.__class__.__name__					,						"NewTokenizer"      )
														self.assertTrue(reloaded_tokenizer.special_attribute_present      )
								else:
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
														self.assertEqual(reloaded_tokenizer.__class__.__name__					,						"NewTokenizer"      )




		@require_tokenizers
		def     lowercase       (				self      ):


								class 			lowerCAmelCase (				__UpperCAmelCase			):
										a							:					Any       =					False


								class 			lowerCAmelCase (				__UpperCAmelCase			):
										a							:					List[str]       =					NewTokenizer
										a							:					List[Any]       =					False

								try:
														AutoConfig.register("custom"					,						UpperCAmelCase_      )
														AutoTokenizer.register(UpperCAmelCase_					,						slow_tokenizer_class=UpperCAmelCase_      )
														AutoTokenizer.register(UpperCAmelCase_					,						fast_tokenizer_class=UpperCAmelCase_      )
														# If remote code is not set, the default is to use local
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizerFast"      )
														self.assertFalse(tokenizer.special_attribute_present      )
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"					,						use_fast=UpperCAmelCase_      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
														self.assertFalse(tokenizer.special_attribute_present      )

														# If remote code is disabled, we load the local one.
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizerFast"      )
														self.assertFalse(tokenizer.special_attribute_present      )
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
														self.assertFalse(tokenizer.special_attribute_present      )

														# If remote is enabled, we load from the Hub
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizerFast"      )
														self.assertTrue(tokenizer.special_attribute_present      )
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer"					,						trust_remote_code=UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
														self.assertTrue(tokenizer.special_attribute_present      )

								finally:
														if "custom" in CONFIG_MAPPING._extra_content:
																				del CONFIG_MAPPING._extra_content["custom"]
														if CustomConfig in TOKENIZER_MAPPING._extra_content:
																				del TOKENIZER_MAPPING._extra_content[CustomConfig]




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
								    "hf-internal-testing/test_dynamic_tokenizer_legacy"					,						trust_remote_code=UpperCAmelCase_      )
								self.assertTrue(tokenizer.special_attribute_present      )
								if is_tokenizers_available():
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizerFast"      )

														# Test we can also load the slow version
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(
														    "hf-internal-testing/test_dynamic_tokenizer_legacy"					,						trust_remote_code=UpperCAmelCase_					,						use_fast=UpperCAmelCase_      )
														self.assertTrue(tokenizer.special_attribute_present      )
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )
								else:
														self.assertEqual(tokenizer.__class__.__name__					,						"NewTokenizer"      )




		def     lowercase       (				self      ):
								with self.assertRaisesRegex(
								    UpperCAmelCase_					,						"bert-base is not a local folder and is not a valid model identifier"      ):
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("bert-base"      )




		def     lowercase       (				self      ):
								with self.assertRaisesRegex(
								    UpperCAmelCase_					,						R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCAmelCase_					,						revision="aaaaaa"      )




		def     lowercase       (				self      ):
								# Make sure we have cached the tokenizer.
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"      )
								with RequestCounter() as counter:
														_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"      )
														self.assertEqual(counter.get_request_count					,						0      )
														self.assertEqual(counter.head_request_count					,						1      )
														self.assertEqual(counter.other_request_count					,						0      )

'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim * 4




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= MCLIPConfig(
								    numDims=self.cross_attention_dim					,						transformerDimensions=self.text_embedder_hidden_size					,						hidden_size=self.text_embedder_hidden_size					,						intermediate_size=37					,						num_attention_heads=4					,						num_hidden_layers=5					,						vocab_size=1_005					,						)

								_SCREAMING_SNAKE_CASE				= MultilingualCLIP(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= text_encoder.eval()

								return text_encoder




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )

								_SCREAMING_SNAKE_CASE				= {
								    "in_channels": 4,
								    # Out channels is double in channels because predicts mean and variance
								    "out_channels": 8,
								    "addition_embed_type": "text_image",
								    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
								    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
								    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
								    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    "layers_per_block": 1,
								    "encoder_hid_dim": self.text_embedder_hidden_size,
								    "encoder_hid_dim_type": "text_image_proj",
								    "cross_attention_dim": self.cross_attention_dim,
								    "attention_head_dim": 4,
								    "resnet_time_scale_shift": "scale_shift",
								    "class_embed_type": None,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "horse",
								    "image": init_image,
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 64,
								    "width": 64,
								    "num_inference_steps": 10,
								    "guidance_scale": 7.0,
								    "strength": 0.2,
								    "output_type": "np",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= pipe(
								    **self.get_dummy_inputs(UpperCamelCase      )					,						return_dict=UpperCamelCase					,						)[0]

								_SCREAMING_SNAKE_CASE				= image[0, -3:, -3:, -1]
								_SCREAMING_SNAKE_CASE				= image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								_SCREAMING_SNAKE_CASE				= np.array(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								assert (
								    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
								), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


@slow
@require_torch_gpu
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
								_SCREAMING_SNAKE_CASE				= "A red cartoon frog, 4k"

								_SCREAMING_SNAKE_CASE				= KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior"					,						torch_dtype=torch.floataa      )
								pipe_prior.to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.Generator(device="cpu"      ).manual_seed(0      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe_prior(
								    UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=5					,						negative_prompt=""					,						).to_tuple()

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )