from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__  :  Tuple											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Optional[int]											=     {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}
class     snake_case	(			snake_case__    ):




					"""simple docstring"""

					snake_case__       =			'''flaubert'''
					snake_case__       =			{
					    '''hidden_size''': '''emb_dim''',
					    '''num_attention_heads''': '''n_heads''',
					    '''num_hidden_layers''': '''n_layers''',
					    '''n_words''': '''vocab_size''',  # For backward compatibility
					}







					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     Dict=False   ,lowerCamelCase__       :     List[str]=0.0   ,lowerCamelCase__       :     Any=30_145   ,lowerCamelCase__       :     Tuple=2_048   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=16   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.1   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     Tuple=False   ,lowerCamelCase__       :     List[str]=False   ,lowerCamelCase__       :     Optional[int]=False   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     Optional[Any]=True   ,lowerCamelCase__       :     Dict=512   ,lowerCamelCase__       :     List[str]=2_048**-0.5   ,lowerCamelCase__       :     str=1e-12   ,lowerCamelCase__       :     Dict=0.0_2   ,lowerCamelCase__       :     Union[str, Any]=0   ,lowerCamelCase__       :     Optional[Any]=1   ,lowerCamelCase__       :     List[Any]=2   ,lowerCamelCase__       :     str=3   ,lowerCamelCase__       :     Optional[int]=5   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     Any="first"   ,lowerCamelCase__       :     int=True   ,lowerCamelCase__       :     Optional[Any]=None   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[str]=0.1   ,lowerCamelCase__       :     Any=5   ,lowerCamelCase__       :     Optional[Any]=5   ,lowerCamelCase__       :     str=0   ,lowerCamelCase__       :     List[Any]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Dict=0   ,**lowerCamelCase__       :     Optional[int]   ,):
									UpperCAmelCase__					=							pre_norm
									UpperCAmelCase__					=							layerdrop
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							emb_dim
									UpperCAmelCase__					=							n_layers
									UpperCAmelCase__					=							n_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							gelu_activation
									UpperCAmelCase__					=							sinusoidal_embeddings
									UpperCAmelCase__					=							causal
									UpperCAmelCase__					=							asm
									UpperCAmelCase__					=							n_langs
									UpperCAmelCase__					=							use_lang_emb
									UpperCAmelCase__					=							layer_norm_eps
									UpperCAmelCase__					=							bos_index
									UpperCAmelCase__					=							eos_index
									UpperCAmelCase__					=							pad_index
									UpperCAmelCase__					=							unk_index
									UpperCAmelCase__					=							mask_index
									UpperCAmelCase__					=							is_encoder
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							embed_init_std
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							start_n_top
									UpperCAmelCase__					=							end_n_top
									UpperCAmelCase__					=							mask_token_id
									UpperCAmelCase__					=							lang_id

									if "n_words" in kwargs:
													UpperCAmelCase__					=							kwargs['n_words']

									super().__init__(pad_token_id=_A   ,bos_token_id=_A   ,**_A      )






class     snake_case	(			snake_case__    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.task == "multiple-choice":
													UpperCAmelCase__					=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]      )




"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			KandinskyPriorPipeline
					snake_case__       =			["prompt"]
					snake_case__       =			["prompt", "negative_prompt"]
					snake_case__       =			[
					    "num_images_per_prompt",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__       =			False







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim







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







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 100







					@property
					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
									return tokenizer







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPTextConfig(
									    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,layer_norm_eps=1e-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_000   ,)
									return CLIPTextModelWithProjection(lowerCamelCase__      )







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									torch.manual_seed(0      )

									UpperCAmelCase__					=							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__					=							PriorTransformer(**lowerCamelCase__      )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__					=							nn.Parameter(torch.ones(model.clip_std.shape      )      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,image_size=224   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=14   ,)

									UpperCAmelCase__					=							CLIPVisionModelWithProjection(lowerCamelCase__      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							CLIPImageProcessor(
									    crop_size=224   ,do_center_crop=lowerCamelCase__   ,do_normalize=lowerCamelCase__   ,do_resize=lowerCamelCase__   ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,resample=3   ,size=224   ,)

									return image_processor







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.dummy_prior
									UpperCAmelCase__					=							self.dummy_image_encoder
									UpperCAmelCase__					=							self.dummy_text_encoder
									UpperCAmelCase__					=							self.dummy_tokenizer
									UpperCAmelCase__					=							self.dummy_image_processor

									UpperCAmelCase__					=							UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,prediction_type='sample'   ,num_train_timesteps=1_000   ,clip_sample=lowerCamelCase__   ,clip_sample_range=1_0.0   ,)

									UpperCAmelCase__					=							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=0      ):
									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									UpperCAmelCase__					=							{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							'cpu'

									UpperCAmelCase__					=							self.get_dummy_components()

									UpperCAmelCase__					=							self.pipeline_class(**lowerCamelCase__      )
									UpperCAmelCase__					=							pipe.to(lowerCamelCase__      )

									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							pipe(**self.get_dummy_inputs(lowerCamelCase__      )      )
									UpperCAmelCase__					=							output.image_embeds

									UpperCAmelCase__					=							pipe(
									    **self.get_dummy_inputs(lowerCamelCase__      )   ,return_dict=lowerCamelCase__   ,)[0]

									UpperCAmelCase__					=							image[0, -10:]
									UpperCAmelCase__					=							image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__					=							np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							True
									UpperCAmelCase__					=							False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowerCamelCase__   ,relax_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)



