
"""simple docstring"""




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


UpperCAmelCase__      :    Optional[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Union[str, Any]    							= {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   '''gptj'''
				__UpperCamelCase      :						Optional[int]					       =   {
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=5_04_00   ,       SCREAMING_SNAKE_CASE__=20_48   ,       SCREAMING_SNAKE_CASE__=40_96   ,       SCREAMING_SNAKE_CASE__=28   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=64   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__="gelu_new"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=1E-5   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       SCREAMING_SNAKE_CASE__=False   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   n_positions
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   n_embd
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   n_layer
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   n_head
											SCREAMING_SNAKE_CASE__						:       Any           =   n_inner
											SCREAMING_SNAKE_CASE__						:       str           =   rotary_dim
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   activation_function
											SCREAMING_SNAKE_CASE__						:       Tuple           =   resid_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   embd_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   attn_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   layer_norm_epsilon
											SCREAMING_SNAKE_CASE__						:       Any           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Tuple           =   use_cache

											SCREAMING_SNAKE_CASE__						:       Dict           =   bos_token_id
											SCREAMING_SNAKE_CASE__						:       str           =   eos_token_id

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






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = "default"   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		str:



											"""simple docstring"""




											super().__init__(SCREAMING_SNAKE_CASE__   ,       task=SCREAMING_SNAKE_CASE__   ,       patching_specs=SCREAMING_SNAKE_CASE__   ,       use_past=SCREAMING_SNAKE_CASE__    )
											if not getattr(self._config   ,       """pad_token_id"""   ,       SCREAMING_SNAKE_CASE__    ):
																		# TODO: how to do that better?
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   0



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
											if self.use_past:
																		self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__   ,       direction="""inputs"""    )
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {0: """batch""", 1: """past_sequence + sequence"""}
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {0: """batch""", 1: """sequence"""}

											return common_inputs



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return self._config.n_layer



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return self._config.n_head



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = -1   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Mapping[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   super(SCREAMING_SNAKE_CASE__   ,       self    ).generate_dummy_inputs(
											    SCREAMING_SNAKE_CASE__   ,       batch_size=SCREAMING_SNAKE_CASE__   ,       seq_length=SCREAMING_SNAKE_CASE__   ,       is_pair=SCREAMING_SNAKE_CASE__   ,       framework=SCREAMING_SNAKE_CASE__    )

											# We need to order the input in the way they appears in the forward()
											SCREAMING_SNAKE_CASE__						:       Optional[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

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

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

											return ordered_inputs



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return 13









"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


