



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__a     =    logging.get_logger(__name__)

__a     =    {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class     lowerCamelCase  (      a_    ):






					'''simple docstring'''





					_A		:      Dict						=					"""blenderbot"""
					_A		:      Union[str, Any]						=					["""past_key_values"""]
					_A		:      Optional[int]						=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






					def __init__(	self:							Optional[Any]						,					snake_case:							Optional[Any]=8_008						,					snake_case:							List[str]=128						,					snake_case:							str=2						,					snake_case:							Optional[Any]=10_240						,					snake_case:							Optional[int]=32						,					snake_case:							Any=24						,					snake_case:							Tuple=10_240						,					snake_case:							Optional[Any]=32						,					snake_case:							Any=0.0						,					snake_case:							List[str]=0.0						,					snake_case:							Tuple=True						,					snake_case:							Tuple=True						,					snake_case:							List[Any]="gelu"						,					snake_case:							Union[str, Any]=2_560						,					snake_case:							Dict=0.1						,					snake_case:							List[str]=0.0						,					snake_case:							str=0.0						,					snake_case:							Tuple=0.0_2						,					snake_case:							Optional[Any]=1						,					snake_case:							Union[str, Any]=False						,					snake_case:							Union[str, Any]=0						,					snake_case:							Dict=1						,					snake_case:							List[Any]=2						,					snake_case:							List[str]=3						,					snake_case:							Optional[Any]=2						,					**snake_case:							Optional[int]						,					)       ->     List[str]:
											snake_case_			:int						=      vocab_size
											snake_case_			:Optional[int]						=      max_position_embeddings
											snake_case_			:int						=      d_model
											snake_case_			:str						=      encoder_ffn_dim
											snake_case_			:Dict						=      encoder_layers
											snake_case_			:Dict						=      encoder_attention_heads
											snake_case_			:Any						=      decoder_ffn_dim
											snake_case_			:Tuple						=      decoder_layers
											snake_case_			:Any						=      decoder_attention_heads
											snake_case_			:Tuple						=      dropout
											snake_case_			:List[str]						=      attention_dropout
											snake_case_			:List[Any]						=      activation_dropout
											snake_case_			:Any						=      activation_function
											snake_case_			:Optional[int]						=      init_std
											snake_case_			:str						=      encoder_layerdrop
											snake_case_			:Optional[int]						=      decoder_layerdrop
											snake_case_			:Union[str, Any]						=      use_cache
											snake_case_			:Optional[int]						=      encoder_layers
											snake_case_			:Optional[int]						=      scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=snake_case__						,					bos_token_id=snake_case__						,					eos_token_id=snake_case__						,					is_encoder_decoder=snake_case__						,					decoder_start_token_id=snake_case__						,					encoder_no_repeat_ngram_size=snake_case__						,					forced_eos_token_id=snake_case__						,					**snake_case__						,					)

class     lowerCamelCase  (      a_    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[int]:


											if self.task in ["default", "seq2seq-lm"]:
																	snake_case_			:Optional[int]						=      OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]						)
																	if self.use_past:
																							snake_case_			:Union[str, Any]						=      {0: """batch"""}
																							snake_case_			:Optional[Any]						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
																	else:
																							snake_case_			:Tuple						=      {0: """batch""", 1: """decoder_sequence"""}
																							snake_case_			:str						=      {0: """batch""", 1: """decoder_sequence"""}
																	if self.use_past:
																							self.fill_with_past_key_values_(snake_case__						,					direction="""inputs"""						)
											elif self.task == "causal-lm":
																	snake_case_			:Any						=      OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	    ]						)
																	if self.use_past:
																							snake_case_,	snake_case_			:List[str]						=      self.num_layers
																							for i in range(snake_case__						):
																													snake_case_			:List[str]						=      {0: """batch""", 2: """past_sequence + sequence"""}
																													snake_case_			:str						=      {0: """batch""", 2: """past_sequence + sequence"""}
											else:
																	snake_case_			:Dict						=      OrderedDict(
																	    [
																	        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
																	        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
																	        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
																	    ]						)

											return common_inputs






					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def        lowerCAmelCase_    (	self:							int						)       ->     Tuple:


											if self.task in ["default", "seq2seq-lm"]:
																	snake_case_			:str						=      super().outputs
											else:
																	snake_case_			:int						=      super(snake_case__						,					self						).outputs
																	if self.use_past:
																							snake_case_,	snake_case_			:Dict						=      self.num_layers
																							for i in range(snake_case__						):
																													snake_case_			:Optional[int]						=      {0: """batch""", 2: """past_sequence + sequence"""}
																													snake_case_			:Union[str, Any]						=      {0: """batch""", 2: """past_sequence + sequence"""}
											return common_outputs






					def        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							Union[str, Any]						,					snake_case:							Tuple = -1						,					snake_case:							List[Any] = -1						,					snake_case:							Tuple = False						,					snake_case:							List[str] = None						,					)       ->     Any:
											snake_case_			:Any						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						)
											# Generate decoder inputs
											snake_case_			:List[str]						=      seq_length if not self.use_past else 1
											snake_case_			:List[str]						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						)
											snake_case_			:Tuple						=      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											snake_case_			:Any						=      dict(**snake_case__						,					**snake_case__						)

											if self.use_past:
																	if not is_torch_available():
																							raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																	else:
																							import torch
																	snake_case_,	snake_case_			:Tuple						=      common_inputs["""input_ids"""].shape
																	snake_case_			:List[str]						=      common_inputs["""decoder_input_ids"""].shape[1]
																	snake_case_,	snake_case_			:Union[str, Any]						=      self.num_attention_heads
																	snake_case_			:Tuple						=      (
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	snake_case_			:str						=      decoder_seq_length
																	snake_case_			:str						=      (
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)
																	snake_case_			:int						=      torch.cat(
																	    [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case__						,					snake_case__						)]						,					dim=1						)
																	snake_case_			:Optional[int]						=      []
																	snake_case_,	snake_case_			:Dict						=      self.num_layers

																	for _ in range(snake_case__						):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(snake_case__						),
																							        torch.zeros(snake_case__						),
																							        torch.zeros(snake_case__						),
																							        torch.zeros(snake_case__						),
																							    )						)
											return common_inputs






					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							int						,					snake_case:							Any = -1						,					snake_case:							Optional[Any] = -1						,					snake_case:							List[str] = False						,					snake_case:							Optional[int] = None						,					)       ->     str:
											snake_case_			:Optional[int]						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						)

											if self.use_past:
																	if not is_torch_available():
																							raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																	else:
																							import torch
																	snake_case_,	snake_case_			:int						=      common_inputs["""input_ids"""].shape
																	snake_case_			:Union[str, Any]						=      seqlen
																	snake_case_,	snake_case_			:Dict						=      self.num_layers
																	snake_case_,	snake_case_			:Optional[Any]						=      self.num_attention_heads
																	snake_case_			:List[str]						=      (
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	snake_case_			:str						=      common_inputs["""attention_mask"""].dtype
																	snake_case_			:Tuple						=      torch.cat(
																	    [common_inputs["""attention_mask"""], torch.ones(snake_case__						,					snake_case__						,					dtype=snake_case__						)]						,					dim=1						)
																	snake_case_			:Any						=      [
																	    (torch.zeros(snake_case__						), torch.zeros(snake_case__						)) for _ in range(snake_case__						)
																	]
											return common_inputs






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							int						,					snake_case:							Any = -1						,					snake_case:							Optional[int] = -1						,					snake_case:							Any = False						,					snake_case:							str = None						,					)       ->     Optional[int]:
											snake_case_			:List[Any]						=      compute_effective_axis_dimension(
											    snake_case__						,					fixed_dimension=OnnxConfig.default_fixed_batch						,					num_token_to_add=0						)

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											snake_case_			:int						=      tokenizer.num_special_tokens_to_add(snake_case__						)
											snake_case_			:Optional[Any]						=      compute_effective_axis_dimension(
											    snake_case__						,					fixed_dimension=OnnxConfig.default_fixed_sequence						,					num_token_to_add=snake_case__						)

											# Generate dummy inputs according to compute batch and sequence
											snake_case_			:Tuple						=      [""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
											snake_case_			:Optional[Any]						=      dict(tokenizer(snake_case__						,					return_tensors=snake_case__						)						)
											return common_inputs






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							List[str]						,					snake_case:							Optional[Any] = -1						,					snake_case:							Optional[Any] = -1						,					snake_case:							Tuple = False						,					snake_case:							Tuple = None						,					)       ->     Optional[int]:


											if self.task in ["default", "seq2seq-lm"]:
																	snake_case_			:Union[str, Any]						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    snake_case__						,					batch_size=snake_case__						,					seq_length=snake_case__						,					is_pair=snake_case__						,					framework=snake_case__						)

											elif self.task == "causal-lm":
																	snake_case_			:List[str]						=      self._generate_dummy_inputs_for_causal_lm(
																	    snake_case__						,					batch_size=snake_case__						,					seq_length=snake_case__						,					is_pair=snake_case__						,					framework=snake_case__						)
											else:
																	snake_case_			:Any						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    snake_case__						,					batch_size=snake_case__						,					seq_length=snake_case__						,					is_pair=snake_case__						,					framework=snake_case__						)

											return common_inputs






					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							Union[str, Any]						,					snake_case:							Optional[int]						,					snake_case:							Tuple						,					snake_case:							List[Any]						)       ->     int:


											if self.task in ["default", "seq2seq-lm"]:
																	snake_case_			:Tuple						=      super()._flatten_past_key_values_(snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						)
											else:
																	snake_case_			:int						=      super(snake_case__						,					self						)._flatten_past_key_values_(
																	    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						)






					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							List[Any]						,					snake_case:							str						)       ->     Tuple:


											if direction not in ["inputs", "outputs"]:
																	raise ValueError(f"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""						)

											snake_case_			:List[Any]						=      """past_key_values""" if direction == """inputs""" else """present"""
											snake_case_,	snake_case_			:int						=      self.num_layers

											snake_case_			:str						=      """past_encoder_sequence"""
											snake_case_			:List[Any]						=      """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

											for i in range(snake_case__						):
																	snake_case_			:Optional[int]						=      {0: """batch""", 2: decoder_sequence}
																	snake_case_			:Any						=      {0: """batch""", 2: decoder_sequence}
																	snake_case_			:Union[str, Any]						=      {0: """batch""", 2: encoder_sequence}
																	snake_case_			:Dict						=      {0: """batch""", 2: encoder_sequence}





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

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					RePaintPipeline
					_A		:      List[Any]						=					IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_A		:      Union[str, Any]						=					PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_A		:      Tuple						=					IMAGE_INPAINTING_BATCH_PARAMS
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[str]:
											torch.manual_seed(0						)
											torch.manual_seed(0						)
											snake_case_			:Union[str, Any]						=      UNetaDModel(
											    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,					)
											snake_case_			:str						=      RePaintScheduler()
											snake_case_			:Any						=      {"""unet""": unet, """scheduler""": scheduler}
											return components






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Dict						,					snake_case:							List[Any]=0						)       ->     Tuple:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_			:int						=      torch.manual_seed(snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_			:Dict						=      np.random.RandomState(snake_case						).standard_normal((1, 3, 32, 32)						)
											snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Dict						=      (image > 0).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Union[str, Any]						=      {
											    """image""": image,
											    """mask_image""": mask,
											    """generator""": generator,
											    """num_inference_steps""": 5,
											    """eta""": 0.0,
											    """jump_length""": 2,
											    """jump_n_sample""": 2,
											    """output_type""": """numpy""",
											}
											return inputs






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											snake_case_			:int						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_			:List[str]						=      self.get_dummy_components()
											snake_case_			:Union[str, Any]						=      RePaintPipeline(**snake_case						)
											snake_case_			:Tuple						=      sd_pipe.to(snake_case						)
											sd_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_			:Tuple						=      self.get_dummy_inputs(snake_case						)
											snake_case_			:Dict						=      sd_pipe(**snake_case						).images
											snake_case_			:List[Any]						=      image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_			:Optional[int]						=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






					@skip_mps
					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""						)
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[Any]:
											return super().test_inference_batch_single_identical()






					@skip_mps
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[Any]:
											return super().test_save_load_optional_components()






					@skip_mps
					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											return super().test_attention_slicing_forward_pass()

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






					'''simple docstring'''





					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     str:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											snake_case_			:Any						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""						)
											snake_case_			:Union[str, Any]						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
											snake_case_			:str						=      load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""						)

											snake_case_			:List[str]						=      """google/ddpm-ema-celebahq-256"""
											snake_case_			:Optional[int]						=      UNetaDModel.from_pretrained(snake_case						)
											snake_case_			:List[Any]						=      RePaintScheduler.from_pretrained(snake_case						)

											snake_case_			:str						=      RePaintPipeline(unet=snake_case						,					scheduler=snake_case						).to(snake_case						)
											repaint.set_progress_bar_config(disable=snake_case						)
											repaint.enable_attention_slicing()

											snake_case_			:int						=      torch.manual_seed(0						)
											snake_case_			:str						=      repaint(
											    snake_case						,					snake_case						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=snake_case						,					output_type="""np"""						,					)
											snake_case_			:List[str]						=      output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).mean() < 1E-2
