
'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						List[str]		 =     KandinskyPipeline
			lowerCamelCase     :						List[str]		 =     [
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			lowerCamelCase     :						str		 =     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			lowerCamelCase     :						Dict		 =     [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase     :						Union[str, Any]		 =     False

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

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

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

			@property
			def     UpperCAmelCase__	(self ):
								return 1_0_0

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
								return tokenizer

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Any       =						MCLIPConfig(
								    numDims=self.cross_attention_dim     ,				transformerDimensions=self.text_embedder_hidden_size     ,				hidden_size=self.text_embedder_hidden_size     ,				intermediate_size=3_7     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=1_0_0_5     ,				)

								lowerCamelCase_   : Union[str, Any]       =						MultilingualCLIP(A )
								lowerCamelCase_   : Dict       =						text_encoder.eval()

								return text_encoder

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

								lowerCamelCase_   : Dict       =						{
								    '''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,
								}

								lowerCamelCase_   : Union[str, Any]       =						UNetaDConditionModel(**A )
								return model

			@property
			def     UpperCAmelCase__	(self ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Dict       =						VQModel(**self.dummy_movq_kwargs )
								return model

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.dummy_text_encoder
								lowerCamelCase_   : Tuple       =						self.dummy_tokenizer
								lowerCamelCase_   : int       =						self.dummy_unet
								lowerCamelCase_   : str       =						self.dummy_movq

								lowerCamelCase_   : str       =						DDIMScheduler(
								    num_train_timesteps=1_0_0_0     ,				beta_schedule='''linear'''     ,				beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				clip_sample=A     ,				set_alpha_to_one=A     ,				steps_offset=1     ,				prediction_type='''epsilon'''     ,				thresholding=A     ,				)

								lowerCamelCase_   : int       =						{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):
								lowerCamelCase_   : Union[str, Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(A ) ).to(A )
								lowerCamelCase_   : List[Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1 ) ).to(A )
								if str(A ).startswith('''mps''' ):
													lowerCamelCase_   : List[str]       =						torch.manual_seed(A )
								else:
													lowerCamelCase_   : Optional[Any]       =						torch.Generator(device=A ).manual_seed(A )
								lowerCamelCase_   : Any       =						{
								    '''prompt''': '''horse''',
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						'''cpu'''

								lowerCamelCase_   : Optional[Any]       =						self.get_dummy_components()

								lowerCamelCase_   : Tuple       =						self.pipeline_class(**A )
								lowerCamelCase_   : List[str]       =						pipe.to(A )

								pipe.set_progress_bar_config(disable=A )

								lowerCamelCase_   : Optional[Any]       =						pipe(**self.get_dummy_inputs(A ) )
								lowerCamelCase_   : int       =						output.images

								lowerCamelCase_   : List[Any]       =						pipe(
								    **self.get_dummy_inputs(A )     ,				return_dict=A     ,				)[0]

								lowerCamelCase_   : List[Any]       =						image[0, -3:, -3:, -1]
								lowerCamelCase_   : List[Any]       =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_   : int       =						np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

								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 		__lowercase			(					unittest.TestCase      ):

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






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

								lowerCamelCase_   : str       =						KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''     ,				torch_dtype=torch.floataa )
								pipe_prior.to(A )

								lowerCamelCase_   : List[Any]       =						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''     ,				torch_dtype=torch.floataa )
								lowerCamelCase_   : Tuple       =						pipeline.to(A )
								pipeline.set_progress_bar_config(disable=A )

								lowerCamelCase_   : List[str]       =						'''red cat, 4k photo'''

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Any       =						pipe_prior(
								    A     ,				generator=A     ,				num_inference_steps=5     ,				negative_prompt=''''''     ,				).to_tuple()

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Optional[Any]       =						pipeline(
								    A     ,				image_embeds=A     ,				negative_image_embeds=A     ,				generator=A     ,				num_inference_steps=1_0_0     ,				output_type='''np'''     ,				)

								lowerCamelCase_   : Union[str, Any]       =						output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(A     ,				A )




'''simple docstring'''


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowercase							:				Optional[Any]					=       logging.getLogger(__name__)




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[int]       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''			)
					parser.add_argument('''--model_name'''     ,    type=_lowercase     ,    default='''transfo-xl-wt103'''     ,    help='''pretrained model name'''			)
					parser.add_argument(
					    '''--split'''     ,    type=_lowercase     ,    default='''test'''     ,    choices=['''all''', '''valid''', '''test''']     ,    help='''which split to evaluate'''			)
					parser.add_argument('''--batch_size'''     ,    type=_lowercase     ,    default=10     ,    help='''batch size'''			)
					parser.add_argument('''--tgt_len'''     ,    type=_lowercase     ,    default=128     ,    help='''number of tokens to predict'''			)
					parser.add_argument('''--ext_len'''     ,    type=_lowercase     ,    default=0     ,    help='''length of the extended context'''			)
					parser.add_argument('''--mem_len'''     ,    type=_lowercase     ,    default=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    help='''max positional embedding index'''			)
					parser.add_argument('''--no_cuda'''     ,    action='''store_true'''     ,    help='''Do not use CUDA even though CUA is available'''			)
					parser.add_argument('''--work_dir'''     ,    type=_lowercase     ,    required=_lowercase     ,    help='''path to the work_dir'''			)
					parser.add_argument('''--no_log'''     ,    action='''store_true'''     ,    help='''do not log the eval result'''			)
					parser.add_argument('''--same_length'''     ,    action='''store_true'''     ,    help='''set same length attention with masking'''			)
					parser.add_argument('''--server_ip'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					parser.add_argument('''--server_port'''     ,    type=_lowercase     ,    default=''''''     ,    help='''Can be used for distant debugging.'''			)
					lowerCamelCase_   : Dict       =						parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach'''			)
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,    redirect_output=_lowercase			)
										ptvsd.wait_for_attach()

					lowerCamelCase_   : List[str]       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
					logger.info('''device: {}'''.format(_lowercase			)			)

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					lowerCamelCase_   : str       =						TransfoXLCorpus.from_pretrained(args.model_name			)

					lowerCamelCase_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : str       =						TransfoXLLMHeadModel.from_pretrained(args.model_name			)
					model.to(_lowercase			)

					logger.info(
					    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
					        args.batch_size     ,    args.tgt_len     ,    args.ext_len     ,    args.mem_len     ,    args.clamp_len			)			)

					model.reset_memory_length(args.mem_len			)
					if args.clamp_len > 0:
										lowerCamelCase_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										lowerCamelCase_   : Tuple       =						evaluate(_lowercase			)
										lowerCamelCase_   : List[Any]       =						evaluate(_lowercase			)
					elif args.split == "valid":
										lowerCamelCase_   : Union[str, Any]       =						evaluate(_lowercase			)
										lowerCamelCase_   : str       =						None
					elif args.split == "test":
										lowerCamelCase_   : Any       =						evaluate(_lowercase			)
										lowerCamelCase_   : int       =						None

					def format_log(_lowercase     ,    _lowercase			):
										lowerCamelCase_   : Optional[Any]       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(_lowercase     ,    _lowercase     ,    math.exp(_lowercase			)			)
										return log_str

					lowerCamelCase_   : List[Any]       =						''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase     ,    '''valid'''			)
					if test_loss is not None:
										log_str += format_log(_lowercase     ,    '''test'''			)

					logger.info('''=''' * 100			)
					logger.info(_lowercase			)
					logger.info('''=''' * 100			)


if __name__ == "__main__":
					main()


