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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase  :					List[str]													=       logging.get_logger(__name__)


lowercase  :					List[Any]													=       {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'codegen'
	_A								=     {
	    'max_position_embeddings': 'n_positions',
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(					self		:int     ,   a		:Any=5_0_4_0_0     ,   a		:Union[str, Any]=2_0_4_8     ,   a		:Dict=2_0_4_8     ,   a		:Union[str, Any]=4_0_9_6     ,   a		:Tuple=2_8     ,   a		:List[Any]=1_6     ,   a		:List[str]=6_4     ,   a		:List[Any]=None     ,   a		:List[Any]="gelu_new"     ,   a		:Optional[int]=0.0     ,   a		:Optional[int]=0.0     ,   a		:List[Any]=0.0     ,   a		:Union[str, Any]=1E-5     ,   a		:Any=0.02     ,   a		:Dict=True     ,   a		:Any=5_0_2_5_6     ,   a		:Tuple=5_0_2_5_6     ,   a		:str=False     ,   **a		:Optional[int]     ,   )		-> List[Any]:
								__UpperCamelCase	:   Tuple			   =					vocab_size
								__UpperCamelCase	:   Any			   =					n_ctx
								__UpperCamelCase	:   int			   =					n_positions
								__UpperCamelCase	:   Union[str, Any]			   =					n_embd
								__UpperCamelCase	:   Optional[Any]			   =					n_layer
								__UpperCamelCase	:   Any			   =					n_head
								__UpperCamelCase	:   List[Any]			   =					n_inner
								__UpperCamelCase	:   Any			   =					rotary_dim
								__UpperCamelCase	:   List[str]			   =					activation_function
								__UpperCamelCase	:   Optional[int]			   =					resid_pdrop
								__UpperCamelCase	:   int			   =					embd_pdrop
								__UpperCamelCase	:   Union[str, Any]			   =					attn_pdrop
								__UpperCamelCase	:   Tuple			   =					layer_norm_epsilon
								__UpperCamelCase	:   Tuple			   =					initializer_range
								__UpperCamelCase	:   List[Any]			   =					use_cache

								__UpperCamelCase	:   Any			   =					bos_token_id
								__UpperCamelCase	:   Any			   =					eos_token_id

								super().__init__(
								    bos_token_id=a     ,   eos_token_id=a     ,   tie_word_embeddings=a     ,   **a						)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Optional[int]     ,   a		:PretrainedConfig     ,   a		:str = "default"     ,   a		:List[PatchingSpec] = None     ,   a		:bool = False     ,   )		-> Tuple:
								super().__init__(a     ,   task=a     ,   patching_specs=a     ,   use_past=a						)
								if not getattr(self._config     ,   "pad_token_id"     ,   a						):
															# TODO: how to do that better?
															__UpperCamelCase	:   Optional[Any]			   =					0







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Mapping[str, Mapping[int, str]]:
								__UpperCamelCase	:   List[str]			   =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
								if self.use_past:
															self.fill_with_past_key_values_(a     ,   direction="inputs"						)
															__UpperCamelCase	:   Optional[Any]			   =					{0: "batch", 1: "past_sequence + sequence"}
								else:
															__UpperCamelCase	:   List[Any]			   =					{0: "batch", 1: "sequence"}

								return common_inputs







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> int:
								return self._config.n_layer







	@property
	def    _lowerCamelCase				(					self		:Any						)		-> int:
								return self._config.n_head







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:PreTrainedTokenizer     ,   a		:int = -1     ,   a		:int = -1     ,   a		:bool = False     ,   a		:Optional[TensorType] = None     ,   )		-> Mapping[str, Any]:
								__UpperCamelCase	:   Dict			   =					super(a     ,   self						).generate_dummy_inputs(
								    a     ,   batch_size=a     ,   seq_length=a     ,   is_pair=a     ,   framework=a						)

								# We need to order the input in the way they appears in the forward()
								__UpperCamelCase	:   Any			   =					OrderedDict({"input_ids": common_inputs["input_ids"]}						)

								# Need to add the past_keys
								if self.use_past:
															if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
															else:
																						import torch

																						__UpperCamelCase   ,						__UpperCamelCase	:   Dict			   =					common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						__UpperCamelCase	:   Optional[Any]			   =					seqlen + 2
																						__UpperCamelCase	:   Dict			   =					(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						__UpperCamelCase	:   Optional[int]			   =					[
																						    (torch.zeros(a						), torch.zeros(a						)) for _ in range(self.num_layers						)
																						]

								__UpperCamelCase	:   Tuple			   =					common_inputs["attention_mask"]
								if self.use_past:
															__UpperCamelCase	:   List[str]			   =					ordered_inputs["attention_mask"].dtype
															__UpperCamelCase	:   Optional[Any]			   =					torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(a     ,   a     ,   dtype=a						)]     ,   dim=1						)

								return ordered_inputs







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								return 1_3

import unittest

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

from diffusers import KandinskyVaaPriorPipeline, 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  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     KandinskyVaaPriorPipeline
	_A								=     ['prompt']
	_A								=     ['prompt', 'negative_prompt']
	_A								=     [
	    'num_images_per_prompt',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	_A								=     False







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Optional[Any]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Tuple:
								return self.time_input_dim







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								return self.time_input_dim * 4







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> str:
								return 1_0_0







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								__UpperCamelCase	:   int			   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
								return tokenizer







	@property
	def    _lowerCamelCase				(					self		:str						)		-> int:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					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=3_7     ,   layer_norm_eps=1E-0_5     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   pad_token_id=1     ,   vocab_size=1_0_0_0     ,   )
								return CLIPTextModelWithProjection(a						)







	@property
	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								torch.manual_seed(0						)

								__UpperCamelCase	:   Optional[int]			   =					{
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_2,
								    "embedding_dim": self.text_embedder_hidden_size,
								    "num_layers": 1,
								}

								__UpperCamelCase	:   List[Any]			   =					PriorTransformer(**a						)
								# 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	:   Optional[int]			   =					nn.Parameter(torch.ones(model.clip_std.shape						)						)
								return model







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> Union[str, Any]:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Union[str, Any]			   =					CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,   image_size=2_2_4     ,   projection_dim=self.text_embedder_hidden_size     ,   intermediate_size=3_7     ,   num_attention_heads=4     ,   num_channels=3     ,   num_hidden_layers=5     ,   patch_size=1_4     ,   )

								__UpperCamelCase	:   int			   =					CLIPVisionModelWithProjection(a						)
								return model







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					CLIPImageProcessor(
								    crop_size=2_2_4     ,   do_center_crop=a     ,   do_normalize=a     ,   do_resize=a     ,   image_mean=[0.48145466, 0.4578275, 0.40821073]     ,   image_std=[0.26862954, 0.26130258, 0.27577711]     ,   resample=3     ,   size=2_2_4     ,   )

								return image_processor







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.dummy_prior
								__UpperCamelCase	:   int			   =					self.dummy_image_encoder
								__UpperCamelCase	:   Tuple			   =					self.dummy_text_encoder
								__UpperCamelCase	:   int			   =					self.dummy_tokenizer
								__UpperCamelCase	:   Optional[Any]			   =					self.dummy_image_processor

								__UpperCamelCase	:   int			   =					UnCLIPScheduler(
								    variance_type="fixed_small_log"     ,   prediction_type="sample"     ,   num_train_timesteps=1_0_0_0     ,   clip_sample=a     ,   clip_sample_range=10.0     ,   )

								__UpperCamelCase	:   List[Any]			   =					{
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "scheduler": scheduler,
								    "image_processor": image_processor,
								}

								return components







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:int     ,   a		:Union[str, Any]=0						)		-> Any:
								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   int			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   List[Any]			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   int			   =					{
								    "prompt": "horse",
								    "generator": generator,
								    "guidance_scale": 4.0,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}
								return inputs







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   int			   =					"cpu"

								__UpperCamelCase	:   List[str]			   =					self.get_dummy_components()

								__UpperCamelCase	:   List[str]			   =					self.pipeline_class(**a						)
								__UpperCamelCase	:   Tuple			   =					pipe.to(a						)

								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   int			   =					pipe(**self.get_dummy_inputs(a						)						)
								__UpperCamelCase	:   int			   =					output.image_embeds

								__UpperCamelCase	:   Optional[int]			   =					pipe(
								    **self.get_dummy_inputs(a						)     ,   return_dict=a     ,   )[0]

								__UpperCamelCase	:   Union[str, Any]			   =					image[0, -1_0:]
								__UpperCamelCase	:   List[str]			   =					image_from_tuple[0, -1_0:]

								assert image.shape == (1, 3_2)

								__UpperCamelCase	:   List[Any]			   =					np.array(
								    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]						)

								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		:int						)		-> Union[str, Any]:
								__UpperCamelCase	:   str			   =					torch_device == "cpu"
								__UpperCamelCase	:   List[str]			   =					True
								__UpperCamelCase	:   List[Any]			   =					False

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







	@skip_mps
	def    _lowerCamelCase				(					self		:Any						)		-> int:
								__UpperCamelCase	:   Optional[Any]			   =					torch_device == "cpu"
								__UpperCamelCase	:   Dict			   =					False

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