
'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase							:				Any					=       logging.get_logger(__name__)

__lowercase							:				Union[str, Any]					=       {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class 		__lowercase			(					_A      ):
			lowerCamelCase     :						Dict		 =     "m2m_100"
			lowerCamelCase     :						str		 =     ["past_key_values"]
			lowerCamelCase     :						int		 =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

			def __init__(self     ,				A=1_2_8_1_1_2     ,				A=1_0_2_4     ,				A=1_2     ,				A=4_0_9_6     ,				A=1_6     ,				A=1_2     ,				A=4_0_9_6     ,				A=1_6     ,				A=0.05     ,				A=0.05     ,				A=True     ,				A=True     ,				A="relu"     ,				A=1_0_2_4     ,				A=0.1     ,				A=0.1     ,				A=0.0     ,				A=0.02     ,				A=2     ,				A=True     ,				A=1     ,				A=0     ,				A=2     ,				**A     ,				):
								lowerCamelCase_   : int       =						vocab_size
								lowerCamelCase_   : Optional[Any]       =						max_position_embeddings
								lowerCamelCase_   : Union[str, Any]       =						d_model
								lowerCamelCase_   : List[str]       =						encoder_ffn_dim
								lowerCamelCase_   : Tuple       =						encoder_layers
								lowerCamelCase_   : List[Any]       =						encoder_attention_heads
								lowerCamelCase_   : Tuple       =						decoder_ffn_dim
								lowerCamelCase_   : Tuple       =						decoder_layers
								lowerCamelCase_   : Optional[int]       =						decoder_attention_heads
								lowerCamelCase_   : Any       =						dropout
								lowerCamelCase_   : Dict       =						attention_dropout
								lowerCamelCase_   : Optional[Any]       =						activation_dropout
								lowerCamelCase_   : int       =						activation_function
								lowerCamelCase_   : Any       =						init_std
								lowerCamelCase_   : Dict       =						encoder_layerdrop
								lowerCamelCase_   : Union[str, Any]       =						decoder_layerdrop
								lowerCamelCase_   : Optional[Any]       =						use_cache
								lowerCamelCase_   : Union[str, Any]       =						encoder_layers
								lowerCamelCase_   : List[Any]       =						scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				is_encoder_decoder=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

class 		__lowercase			(					_A      ):

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ] )

								if self.use_past:
													lowerCamelCase_   : List[str]       =						{0: '''batch'''}
													lowerCamelCase_   : List[str]       =						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
													lowerCamelCase_   : Tuple       =						{0: '''batch''', 1: '''decoder_sequence'''}
													lowerCamelCase_   : List[str]       =						{0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
													self.fill_with_past_key_values_(UpperCamelCase__     ,				direction='''inputs''' )
								return common_inputs

			def     UpperCAmelCase__	(self     ,				A     ,				A = -1     ,				A = -1     ,				A = False     ,				A = None     ,				):
								lowerCamelCase_   : int       =						compute_effective_axis_dimension(
								    UpperCamelCase__     ,				fixed_dimension=OnnxConfig.default_fixed_batch     ,				num_token_to_add=0 )

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								lowerCamelCase_   : Union[str, Any]       =						tokenizer.num_special_tokens_to_add(UpperCamelCase__ )
								lowerCamelCase_   : str       =						compute_effective_axis_dimension(
								    UpperCamelCase__     ,				fixed_dimension=OnnxConfig.default_fixed_sequence     ,				num_token_to_add=UpperCamelCase__ )

								# Generate dummy inputs according to compute batch and sequence
								lowerCamelCase_   : str       =						[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
								lowerCamelCase_   : Optional[int]       =						dict(tokenizer(UpperCamelCase__     ,				return_tensors=UpperCamelCase__ ) )
								return common_inputs

			def     UpperCAmelCase__	(self     ,				A     ,				A = -1     ,				A = -1     ,				A = False     ,				A = None     ,				):
								lowerCamelCase_   : Optional[Any]       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__ )

								# Generate decoder inputs
								lowerCamelCase_   : Tuple       =						seq_length if not self.use_past else 1
								lowerCamelCase_   : Optional[int]       =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__ )
								lowerCamelCase_   : Dict       =						{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								lowerCamelCase_   : List[Any]       =						dict(**UpperCamelCase__     ,				**UpperCamelCase__ )

								if self.use_past:
													if not is_torch_available():
																		raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
													else:
																		import torch
													lowerCamelCase_, lowerCamelCase_   : int       =						common_inputs['''input_ids'''].shape
													lowerCamelCase_   : str       =						common_inputs['''decoder_input_ids'''].shape[1]
													lowerCamelCase_, lowerCamelCase_   : List[str]       =						self.num_attention_heads
													lowerCamelCase_   : List[Any]       =						(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													lowerCamelCase_   : Dict       =						decoder_seq_length + 3
													lowerCamelCase_   : Union[str, Any]       =						(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													lowerCamelCase_   : int       =						torch.cat(
													    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__     ,				UpperCamelCase__ )]     ,				dim=1 )

													lowerCamelCase_   : Any       =						[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													lowerCamelCase_, lowerCamelCase_   : List[Any]       =						self.num_layers
													lowerCamelCase_   : str       =						min(UpperCamelCase__     ,				UpperCamelCase__ )
													lowerCamelCase_   : Dict       =						max(UpperCamelCase__     ,				UpperCamelCase__ ) - min_num_layers
													lowerCamelCase_   : Any       =						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

													for _ in range(UpperCamelCase__ ):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(UpperCamelCase__ ),
																		        torch.zeros(UpperCamelCase__ ),
																		        torch.zeros(UpperCamelCase__ ),
																		        torch.zeros(UpperCamelCase__ ),
																		    ) )
													# TODO: test this.
													lowerCamelCase_   : str       =						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
													for _ in range(UpperCamelCase__     ,				UpperCamelCase__ ):
																		common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__ ), torch.zeros(UpperCamelCase__ )) )
								return common_inputs






			lowerCamelCase     :						Optional[int]		 =     _generate_dummy_inputs_for_default_and_seqaseq_lm




'''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_, 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 )


