"""simple docstring"""






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


UpperCamelCase     					=     logging.get_logger(__name__)

UpperCamelCase     					=     {
    '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   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					Union[str, Any]						      =							"""blenderbot"""
	lowerCAmelCase__    :					Optional[Any]						      =							["""past_key_values"""]
	lowerCAmelCase__    :					Dict						      =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=8_0_0_8       ,     _SCREAMING_SNAKE_CASE=1_2_8       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=1_0_2_4_0       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=2_4       ,     _SCREAMING_SNAKE_CASE=1_0_2_4_0       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE="gelu"       ,     _SCREAMING_SNAKE_CASE=2_5_6_0       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.0_2       ,     _SCREAMING_SNAKE_CASE=1       ,     _SCREAMING_SNAKE_CASE=False       ,     _SCREAMING_SNAKE_CASE=0       ,     _SCREAMING_SNAKE_CASE=1       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=3       ,     _SCREAMING_SNAKE_CASE=2       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Union[str, Any]:
							a_							:			List[str]     		=					vocab_size
							a_							:			List[str]     		=					max_position_embeddings
							a_							:			List[Any]     		=					d_model
							a_							:			Tuple     		=					encoder_ffn_dim
							a_							:			Tuple     		=					encoder_layers
							a_							:			Tuple     		=					encoder_attention_heads
							a_							:			List[Any]     		=					decoder_ffn_dim
							a_							:			str     		=					decoder_layers
							a_							:			Tuple     		=					decoder_attention_heads
							a_							:			Dict     		=					dropout
							a_							:			str     		=					attention_dropout
							a_							:			List[str]     		=					activation_dropout
							a_							:			List[str]     		=					activation_function
							a_							:			str     		=					init_std
							a_							:			List[Any]     		=					encoder_layerdrop
							a_							:			Tuple     		=					decoder_layerdrop
							a_							:			Optional[int]     		=					use_cache
							a_							:			int     		=					encoder_layers
							a_							:			Optional[int]     		=					scale_embedding  # scale factor will be sqrt(d_model) if True

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




class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""







	@property
	def       A    (			self )    ->	Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													a_							:			List[Any]     		=					OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ] )
													if self.use_past:
																			a_							:			Union[str, Any]     		=					{0: "batch"}
																			a_							:			Any     		=					{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																			a_							:			Optional[int]     		=					{0: "batch", 1: "decoder_sequence"}
																			a_							:			Tuple     		=					{0: "batch", 1: "decoder_sequence"}
													if self.use_past:
																			self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE       ,     direction="inputs" )
							elif self.task == "causal-lm":
													a_							:			Dict     		=					OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ] )
													if self.use_past:
																			a_		,					a_							:			Optional[int]     		=					self.num_layers
																			for i in range(_SCREAMING_SNAKE_CASE ):
																									a_							:			Any     		=					{0: "batch", 2: "past_sequence + sequence"}
																									a_							:			List[Any]     		=					{0: "batch", 2: "past_sequence + sequence"}
							else:
													a_							:			Optional[Any]     		=					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       A    (			self )    ->	Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													a_							:			Optional[Any]     		=					super().outputs
							else:
													a_							:			str     		=					super(_SCREAMING_SNAKE_CASE       ,     self ).outputs
													if self.use_past:
																			a_		,					a_							:			List[str]     		=					self.num_layers
																			for i in range(_SCREAMING_SNAKE_CASE ):
																									a_							:			Any     		=					{0: "batch", 2: "past_sequence + sequence"}
																									a_							:			Optional[Any]     		=					{0: "batch", 2: "past_sequence + sequence"}
							return common_outputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = None       ,     )    ->	Mapping[str, Any]:
							a_							:			List[str]     		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							# Generate decoder inputs
							a_							:			Union[str, Any]     		=					seq_length if not self.use_past else 1
							a_							:			List[str]     		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							a_							:			str     		=					{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							a_							:			int     		=					dict(**_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_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
													a_		,					a_							:			List[Any]     		=					common_inputs["input_ids"].shape
													a_							:			Any     		=					common_inputs["decoder_input_ids"].shape[1]
													a_		,					a_							:			Optional[int]     		=					self.num_attention_heads
													a_							:			Union[str, Any]     		=					(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													a_							:			str     		=					decoder_seq_length
													a_							:			int     		=					(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)
													a_							:			List[Any]     		=					torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )]       ,     dim=1 )
													a_							:			Optional[Any]     		=					[]
													a_		,					a_							:			Optional[int]     		=					self.num_layers

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = None       ,     )    ->	Mapping[str, Any]:
							a_							:			Tuple     		=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_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
													a_		,					a_							:			Tuple     		=					common_inputs["input_ids"].shape
													a_							:			Optional[Any]     		=					seqlen
													a_		,					a_							:			int     		=					self.num_layers
													a_		,					a_							:			int     		=					self.num_attention_heads
													a_							:			Dict     		=					(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													a_							:			Union[str, Any]     		=					common_inputs["attention_mask"].dtype
													a_							:			str     		=					torch.cat(
													    [common_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=_SCREAMING_SNAKE_CASE )]       ,     dim=1 )
													a_							:			Dict     		=					[
													    (torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) for _ in range(_SCREAMING_SNAKE_CASE )
													]
							return common_inputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = 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_							:			int     		=					compute_effective_axis_dimension(
							    _SCREAMING_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
							a_							:			Any     		=					tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
							a_							:			List[Any]     		=					compute_effective_axis_dimension(
							    _SCREAMING_SNAKE_CASE       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=_SCREAMING_SNAKE_CASE )

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







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = -1       ,     _SCREAMING_SNAKE_CASE = False       ,     _SCREAMING_SNAKE_CASE = None       ,     )    ->	Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
													a_							:			str     		=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    _SCREAMING_SNAKE_CASE       ,     batch_size=_SCREAMING_SNAKE_CASE       ,     seq_length=_SCREAMING_SNAKE_CASE       ,     is_pair=_SCREAMING_SNAKE_CASE       ,     framework=_SCREAMING_SNAKE_CASE )

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

							return common_inputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Tuple:
							if self.task in ["default", "seq2seq-lm"]:
													a_							:			List[str]     		=					super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
							else:
													a_							:			Any     		=					super(_SCREAMING_SNAKE_CASE       ,     self )._flatten_past_key_values_(
													    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Optional[int]:
							if direction not in ["inputs", "outputs"]:
													raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given''' )

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

							a_							:			Tuple     		=					"past_encoder_sequence"
							a_							:			Dict     		=					"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

							for i in range(_SCREAMING_SNAKE_CASE ):
													a_							:			List[Any]     		=					{0: "batch", 2: decoder_sequence}
													a_							:			Tuple     		=					{0: "batch", 2: decoder_sequence}
													a_							:			int     		=					{0: "batch", 2: encoder_sequence}
													a_							:			Optional[int]     		=					{0: "batch", 2: encoder_sequence}





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




