
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	lowercase							=						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls   ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__   ,**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@classmethod
							def  A__      (				cls   ,A__   ,A__   ,**A__):
												return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**A__)





							def  A__      (				self):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output






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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=ShapEPipeline
							lowercase_       :       List[Any]       					=['''prompt''']
							lowercase_       :       int       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Optional[int]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




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




							@property
							def  A__      (				self):
												return 8




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




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




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

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_6,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 3_2,
												    '''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,
												}

												lowercase							=						PriorTransformer(**A__)
												return model




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

												lowercase							=						{
												    '''param_shapes''': (
												        (self.renderer_dim, 9_3),
												        (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''': 1_2,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												lowercase							=						ShapERenderer(**A__)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_renderer

												lowercase							=						HeunDiscreteScheduler(
												    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=A__   ,clip_sample=A__   ,clip_sample_range=1.0   ,)
												lowercase							=						{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 3_2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

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

												assert image.shape == (2_0, 3_2, 3_2, 3)

												lowercase							=						np.array(
												    [
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												    ])

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




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




							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True

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





							def  A__      (				self):
												lowercase							=						self.get_dummy_components()
												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						1
												lowercase							=						2

												lowercase							=						self.get_dummy_inputs(A__)

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

												lowercase							=						pipe(**A__   ,num_images_per_prompt=A__)[0]

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







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




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





							def  A__      (				self):
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy''')
												lowercase							=						ShapEPipeline.from_pretrained('''openai/shap-e''')
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						torch.Generator(device=A__).manual_seed(0)

												lowercase							=						pipe(
												    '''a shark'''   ,generator=A__   ,guidance_scale=15.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

												assert images.shape == (2_0, 6_4, 6_4, 3)

												assert_mean_pixel_difference(A__   ,A__)




