




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Any										= IFImgaImgPipeline
				SCREAMING_SNAKE_CASE_	:							List[str]										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_	:							Dict										= PipelineTesterMixin.required_optional_params - {"""latents"""}





				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							return self._get_dummy_components()





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					str=0							) ->					Union[str, Any]:
							if str(__lowerCamelCase							).startswith("mps"							):
										a   =      torch.manual_seed(__lowerCamelCase							)
							else:
										a   =      torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)

							a   =      floats_tensor((1, 3, 32, 32) ,				rng=random.Random(__lowerCamelCase							)							).to(__lowerCamelCase							)

							a   =      {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs





				def 							__UpperCAmelCase							(				self    :					int							) ->					List[str]:
							self._test_save_load_optional_components()





				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available() ,				reason="XFormers attention is only available with CUDA and `xformers` installed" ,				)
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					int:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)





				@unittest.skipIf(torch_device != "cuda" ,				reason="float16 requires CUDA"							)
				def 							__UpperCAmelCase							(				self    :					str							) ->					Optional[Any]:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1							)





				@unittest.skipIf(torch_device != "cuda" ,				reason="float16 requires CUDA"							)
				def 							__UpperCAmelCase							(				self    :					str							) ->					List[str]:
							super().test_floataa_inference(expected_max_diff=1e-1							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					List[str]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Any:
							self._test_save_load_local()





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[int]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2 ,				)












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

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


__lowerCAmelCase		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Optional[int]	      =		{
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							int										= """mbart"""
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_	:							Any										= {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





				def __init__(				self    :					List[Any] ,				__lowerCamelCase    :					str=5_02_65 ,				__lowerCamelCase    :					List[Any]=10_24 ,				__lowerCamelCase    :					Optional[Any]=12 ,				__lowerCamelCase    :					List[Any]=40_96 ,				__lowerCamelCase    :					int=16 ,				__lowerCamelCase    :					List[Any]=12 ,				__lowerCamelCase    :					Tuple=40_96 ,				__lowerCamelCase    :					Tuple=16 ,				__lowerCamelCase    :					Optional[Any]=0.0 ,				__lowerCamelCase    :					int=0.0 ,				__lowerCamelCase    :					Any=True ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					str="gelu" ,				__lowerCamelCase    :					Tuple=10_24 ,				__lowerCamelCase    :					Any=0.1 ,				__lowerCamelCase    :					Dict=0.0 ,				__lowerCamelCase    :					Tuple=0.0 ,				__lowerCamelCase    :					Optional[int]=0.02 ,				__lowerCamelCase    :					Dict=0.0 ,				__lowerCamelCase    :					Union[str, Any]=False ,				__lowerCamelCase    :					Optional[int]=1 ,				__lowerCamelCase    :					Tuple=0 ,				__lowerCamelCase    :					List[Any]=2 ,				__lowerCamelCase    :					Dict=2 ,				**__lowerCamelCase    :					Optional[int] ,				) ->					Optional[int]:
							a   =      vocab_size
							a   =      max_position_embeddings
							a   =      d_model
							a   =      encoder_ffn_dim
							a   =      encoder_layers
							a   =      encoder_attention_heads
							a   =      decoder_ffn_dim
							a   =      decoder_layers
							a   =      decoder_attention_heads
							a   =      dropout
							a   =      attention_dropout
							a   =      activation_dropout
							a   =      activation_function
							a   =      init_std
							a   =      encoder_layerdrop
							a   =      decoder_layerdrop
							a   =      classifier_dropout
							a   =      use_cache
							a   =      encoder_layers
							a   =      scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=__lowerCamelCase ,				bos_token_id=__lowerCamelCase ,				eos_token_id=__lowerCamelCase ,				is_encoder_decoder=__lowerCamelCase ,				forced_eos_token_id=__lowerCamelCase ,				**__lowerCamelCase ,				)




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
										a   =      OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]							)

										if self.use_past:
													a   =      {0: "batch"}
													a   =      {0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
													a   =      {0: "batch", 1: "decoder_sequence"}
													a   =      {0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(__lowerCamelCase ,				direction="inputs"							)
							elif self.task == "causal-lm":
										# TODO: figure this case out.
										a   =      OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]							)
										if self.use_past:
													a   ,		a   =      self.num_layers
													for i in range(__lowerCamelCase							):
																a   =      {0: "batch", 2: "past_sequence + sequence"}
																a   =      {0: "batch", 2: "past_sequence + sequence"}
							else:
										a   =      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
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
										a   =      super().outputs
							else:
										a   =      super(__lowerCamelCase ,				self							).outputs
										if self.use_past:
													a   ,		a   =      self.num_layers
													for i in range(__lowerCamelCase							):
																a   =      {0: "batch", 2: "past_sequence + sequence"}
																a   =      {0: "batch", 2: "past_sequence + sequence"}
							return common_outputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					Mapping[str, Any]:
							a   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)

							# Generate decoder inputs
							a   =      seq_length if not self.use_past else 1
							a   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)
							a   =      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							a   =      dict(**__lowerCamelCase ,				**__lowerCamelCase							)

							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
										else:
													import torch
										a   ,		a   =      common_inputs["input_ids"].shape
										a   =      common_inputs["decoder_input_ids"].shape[1]
										a   ,		a   =      self.num_attention_heads
										a   =      (
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										a   =      decoder_seq_length + 3
										a   =      (
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										a   =      torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase ,				__lowerCamelCase							)] ,				dim=1							)

										a   =      []
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										a   ,		a   =      self.num_layers
										a   =      min(__lowerCamelCase ,				__lowerCamelCase							)
										a   =      max(__lowerCamelCase ,				__lowerCamelCase							) - min_num_layers
										a   =      "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

										for _ in range(__lowerCamelCase							):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(__lowerCamelCase							),
													        torch.zeros(__lowerCamelCase							),
													        torch.zeros(__lowerCamelCase							),
													        torch.zeros(__lowerCamelCase							),
													    )							)
										# TODO: test this.
										a   =      encoder_shape if remaining_side_name == "encoder" else decoder_shape
										for _ in range(__lowerCamelCase ,				__lowerCamelCase							):
													common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase							), torch.zeros(__lowerCamelCase							))							)
							return common_inputs





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					Mapping[str, Any]:
							a   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)

							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
										else:
													import torch
										a   ,		a   =      common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										a   =      seqlen + 2
										a   ,		a   =      self.num_layers
										a   ,		a   =      self.num_attention_heads
										a   =      (
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										a   =      common_inputs["attention_mask"].dtype
										a   =      torch.cat(
										    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase ,				__lowerCamelCase ,				dtype=__lowerCamelCase							)] ,				dim=1							)
										a   =      [
										    (torch.zeros(__lowerCamelCase							), torch.zeros(__lowerCamelCase							)) for _ in range(__lowerCamelCase							)
										]
							return common_inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					Mapping[str, Any]:
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							a   =      compute_effective_axis_dimension(
							    __lowerCamelCase ,				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
							a   =      tokenizer.num_special_tokens_to_add(__lowerCamelCase							)
							a   =      compute_effective_axis_dimension(
							    __lowerCamelCase ,				fixed_dimension=OnnxConfig.default_fixed_sequence ,				num_token_to_add=__lowerCamelCase							)

							# Generate dummy inputs according to compute batch and sequence
							a   =      [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
							a   =      dict(tokenizer(__lowerCamelCase ,				return_tensors=__lowerCamelCase							)							)
							return common_inputs





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					PreTrainedTokenizer ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					int = -1 ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[TensorType] = None ,				) ->					Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
										a   =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    __lowerCamelCase ,				batch_size=__lowerCamelCase ,				seq_length=__lowerCamelCase ,				is_pair=__lowerCamelCase ,				framework=__lowerCamelCase							)

							elif self.task == "causal-lm":
										a   =      self._generate_dummy_inputs_for_causal_lm(
										    __lowerCamelCase ,				batch_size=__lowerCamelCase ,				seq_length=__lowerCamelCase ,				is_pair=__lowerCamelCase ,				framework=__lowerCamelCase							)
							else:
										a   =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __lowerCamelCase ,				batch_size=__lowerCamelCase ,				seq_length=__lowerCamelCase ,				is_pair=__lowerCamelCase ,				framework=__lowerCamelCase							)

							return common_inputs





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[Any]							) ->					Dict:
							if self.task in ["default", "seq2seq-lm"]:
										a   =      super()._flatten_past_key_values_(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)
							else:
										a   =      super(__lowerCamelCase ,				self							)._flatten_past_key_values_(
										    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)






