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


_lowerCAmelCase       =		logging.get_logger(__name__)


_lowerCAmelCase       =		{
    "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 ):
							UpperCAmelCase				   =     "codegen"
							UpperCAmelCase				   =     {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}







							def __init__(   self      :     int ,			_A      :     Optional[Any]=5_0400 ,			_A      :     str=2048 ,			_A      :     Tuple=2048 ,			_A      :     List[Any]=4096 ,			_A      :     Optional[Any]=28 ,			_A      :     Union[str, Any]=16 ,			_A      :     List[str]=64 ,			_A      :     Union[str, Any]=None ,			_A      :     Any="gelu_new" ,			_A      :     List[str]=0.0 ,			_A      :     str=0.0 ,			_A      :     Any=0.0 ,			_A      :     Tuple=1e-5 ,			_A      :     Dict=0.02 ,			_A      :     int=True ,			_A      :     List[str]=5_0256 ,			_A      :     Optional[int]=5_0256 ,			_A      :     Dict=False ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     n_ctx
												_UpperCamelCase			     =     n_positions
												_UpperCamelCase			     =     n_embd
												_UpperCamelCase			     =     n_layer
												_UpperCamelCase			     =     n_head
												_UpperCamelCase			     =     n_inner
												_UpperCamelCase			     =     rotary_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     resid_pdrop
												_UpperCamelCase			     =     embd_pdrop
												_UpperCamelCase			     =     attn_pdrop
												_UpperCamelCase			     =     layer_norm_epsilon
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     use_cache

												_UpperCamelCase			     =     bos_token_id
												_UpperCamelCase			     =     eos_token_id

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




class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     List[Any] ,			_A      :     PretrainedConfig ,			_A      :     str = "default" ,			_A      :     List[PatchingSpec] = None ,			_A      :     bool = False ,			):
												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			     =     0







							@property
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
												if self.use_past:
																	self.fill_with_past_key_values_(_A ,			direction='''inputs'''		)
																	_UpperCamelCase			     =     {0: '''batch''', 1: '''past_sequence + sequence'''}
												else:
																	_UpperCamelCase			     =     {0: '''batch''', 1: '''sequence'''}

												return common_inputs







							@property
							def  UpperCamelCase_						(   self      :     Tuple		):
												return self._config.n_layer







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												return self._config.n_head







							def  UpperCamelCase_						(   self      :     str ,			_A      :     PreTrainedTokenizer ,			_A      :     int = -1 ,			_A      :     int = -1 ,			_A      :     bool = False ,			_A      :     Optional[TensorType] = None ,			):
												_UpperCamelCase			     =     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			     =     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			     =     common_inputs['''input_ids'''].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase			     =     seqlen + 2
																						_UpperCamelCase			     =     (
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						_UpperCamelCase			     =     [
																						    (torch.zeros(_A		), torch.zeros(_A		)) for _ in range(self.num_layers		)
																						]

												_UpperCamelCase			     =     common_inputs['''attention_mask''']
												if self.use_past:
																	_UpperCamelCase			     =     ordered_inputs['''attention_mask'''].dtype
																	_UpperCamelCase			     =     torch.cat(
																	    [ordered_inputs['''attention_mask'''], torch.ones(_A ,			_A ,			dtype=_A		)] ,			dim=1		)

												return ordered_inputs







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 13

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







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







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







							@property
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
												return tokenizer







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												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=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 16,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 32,
												    '''embedding_proj_dim''': self.text_embedder_hidden_size,
												    '''time_embed_dim''': self.time_embed_dim,
												    '''num_layers''': 1,
												    '''clip_embed_dim''': self.time_input_dim * 2,
												    '''additional_embeddings''': 0,
												    '''time_embed_act_fn''': '''gelu''',
												    '''norm_in_type''': '''layer''',
												    '''encoder_hid_proj_type''': None,
												    '''added_emb_type''': None,
												}

												_UpperCamelCase			     =     PriorTransformer(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''param_shapes''': (
												        (self.renderer_dim, 93),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												        (self.renderer_dim, 8),
												    ),
												    '''d_latent''': self.time_input_dim,
												    '''d_hidden''': self.renderer_dim,
												    '''n_output''': 12,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												_UpperCamelCase			     =     ShapERenderer(**_A		)
												return model







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.dummy_prior
												_UpperCamelCase			     =     self.dummy_text_encoder
												_UpperCamelCase			     =     self.dummy_tokenizer
												_UpperCamelCase			     =     self.dummy_renderer

												_UpperCamelCase			     =     HeunDiscreteScheduler(
												    beta_schedule='''exp''' ,			num_train_timesteps=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     '''cpu'''

												_UpperCamelCase			     =     self.get_dummy_components()

												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)

												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     pipe(**self.get_dummy_inputs(_A		)		)
												_UpperCamelCase			     =     output.images[0]
												_UpperCamelCase			     =     image[0, -3:, -3:, -1]

												assert image.shape == (20, 32, 32, 3)

												_UpperCamelCase			     =     np.array(
												    [
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												    ]		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

												self._test_inference_batch_single_identical(
												    batch_size=2 ,			test_max_difference=_A ,			relax_max_difference=_A ,			)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.get_dummy_components()
												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     1
												_UpperCamelCase			     =     2

												_UpperCamelCase			     =     self.get_dummy_inputs(_A		)

												for key in inputs.keys():
																	if key in self.batch_params:
																						_UpperCamelCase			     =     batch_size * [inputs[key]]

												_UpperCamelCase			     =     pipe(**_A ,			num_images_per_prompt=_A		)[0]

												assert images.shape[0] == batch_size * num_images_per_prompt




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







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







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''		)
												_UpperCamelCase			     =     ShapEPipeline.from_pretrained('''openai/shap-e'''		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(0		)

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			guidance_scale=15.0 ,			num_inference_steps=64 ,			frame_size=64 ,			output_type='''np''' ,			).images[0]

												assert images.shape == (20, 64, 64, 3)

												assert_mean_pixel_difference(_A ,			_A		)
