
"""simple docstring"""

from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
					from .data.data_collator import DataCollator
					from .modeling_utils import PreTrainedModel
					from .tokenization_utils_base import PreTrainedTokenizerBase
					from .trainer_callback import TrainerCallback
					from .trainer_utils import EvalPrediction, PredictionOutput
					from .training_args import TrainingArguments


_A            = logging.get_logger(__name__)






class        _lowercase   (				__UpperCAmelCase		):




	def __init__( self		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = (None, None)		, UpperCAmelCase_ = None		, )		->							str:
			super().__init__(
			    model=snake_case__		, args=snake_case__		, data_collator=snake_case__		, train_dataset=snake_case__		, eval_dataset=snake_case__		, tokenizer=snake_case__		, model_init=snake_case__		, compute_metrics=snake_case__		, callbacks=snake_case__		, optimizers=snake_case__		, preprocess_logits_for_metrics=snake_case__		, )

			# Override self.model.generation_config if a GenerationConfig is specified in args.
			# Priority: args.generation_config > model.generation_config > default GenerationConfig.
			if self.args.generation_config is not None:
					lowerCamelCase     :			List[Any]		=    self.load_generation_config(self.args.generation_config					)
					lowerCamelCase     :			Tuple		=    gen_config




	@staticmethod
	def 			_UpperCamelCase    ( UpperCAmelCase_					)		->							Optional[Any]:

			# GenerationConfig provided, nothing to do
			if isinstance(snake_case__		, snake_case__					):
					return deepcopy(snake_case__					)

			# str or Path
			lowerCamelCase     :			int		=    Path(snake_case__					) if isinstance(snake_case__		, snake_case__					) else gen_config_arg
			lowerCamelCase     :			Optional[int]		=    None

			# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
			# This step is required in order to determine config_file_name
			if pretrained_model_name.is_file():
					lowerCamelCase     :			int		=    pretrained_model_name.name
					lowerCamelCase     :			Dict		=    pretrained_model_name.parent
			# dir path
			elif pretrained_model_name.is_dir():
					pass
			# model id or URL
			else:
					lowerCamelCase     :			str		=    gen_config_arg

			lowerCamelCase     :			Dict		=    GenerationConfig.from_pretrained(snake_case__		, snake_case__					)
			return gen_config




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_ = None		, UpperCAmelCase_ = None		, UpperCAmelCase_ = "eval"		, **UpperCAmelCase_		, )		->							Tuple:
			lowerCamelCase     :			Optional[Any]		=    gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
					lowerCamelCase     :			Tuple		=    self.args.generation_max_length
			lowerCamelCase     :			List[str]		=    (
			    gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			lowerCamelCase     :			Union[str, Any]		=    gen_kwargs

			return super().evaluate(snake_case__		, ignore_keys=snake_case__		, metric_key_prefix=snake_case__					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = None		, UpperCAmelCase_ = "test"		, **UpperCAmelCase_		, )		->							Optional[int]:
			lowerCamelCase     :			int		=    gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
					lowerCamelCase     :			Optional[Any]		=    self.args.generation_max_length
			lowerCamelCase     :			Optional[Any]		=    (
			    gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			lowerCamelCase     :			Tuple		=    gen_kwargs

			return super().predict(snake_case__		, ignore_keys=snake_case__		, metric_key_prefix=snake_case__					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = None		, **UpperCAmelCase_		, )		->							Tuple:

			if not self.args.predict_with_generate or prediction_loss_only:
					return super().prediction_step(
					    snake_case__		, snake_case__		, prediction_loss_only=snake_case__		, ignore_keys=snake_case__					)

			lowerCamelCase     :			int		=    "labels" in inputs
			lowerCamelCase     :			Optional[Any]		=    self._prepare_inputs(snake_case__					)

			# XXX: adapt synced_gpus for fairscale as well
			# Priority (handled in generate):
			# gen_kwargs > model.generation_config > default GenerationConfig()

			if len(snake_case__					) == 0 and hasattr(self		, '_gen_kwargs'					):
					lowerCamelCase     :			str		=    self._gen_kwargs.copy()

			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
					lowerCamelCase     :			Any		=    self.model.config.max_length
			lowerCamelCase     :			Dict		=    (
			    gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'					) is not None else self.model.config.num_beams
			)
			lowerCamelCase     :			List[str]		=    True if is_deepspeed_zeroa_enabled() else False
			lowerCamelCase     :			List[str]		=    (
			    gen_kwargs["synced_gpus"] if gen_kwargs.get('synced_gpus'					) is not None else default_synced_gpus
			)

			# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
			# (otherwise, it would continue generating from the padded `decoder_input_ids`)
			if (
			    "labels" in inputs
			    and "decoder_input_ids" in inputs
			    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
			):
					lowerCamelCase     :			Dict		=    {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
			lowerCamelCase     :			Union[str, Any]		=    self.model.generate(**snake_case__		, **snake_case__					)

			# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
			# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
			# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
			if self.model.generation_config._from_model_config:
					lowerCamelCase     :			str		=    False

			# Retrieves GenerationConfig from model.generation_config
			lowerCamelCase     :			List[str]		=    self.model.generation_config
			# in case the batch is shorter than max length, the output should be padded
			if generated_tokens.shape[-1] < gen_config.max_length:
					lowerCamelCase     :			Tuple		=    self._pad_tensors_to_max_len(snake_case__		, gen_config.max_length					)
			elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
					lowerCamelCase     :			Optional[int]		=    self._pad_tensors_to_max_len(snake_case__		, gen_config.max_new_tokens + 1					)

			with torch.no_grad():
					if has_labels:
							with self.compute_loss_context_manager():
									lowerCamelCase     :			Tuple		=    model(**snake_case__					)
							if self.label_smoother is not None:
									lowerCamelCase     :			Optional[Any]		=    self.label_smoother(snake_case__		, inputs['labels']					).mean().detach()
							else:
									lowerCamelCase     :			Union[str, Any]		=    (outputs["loss"] if isinstance(snake_case__		, snake_case__					) else outputs[0]).mean().detach()
					else:
							lowerCamelCase     :			Union[str, Any]		=    None

			if self.args.prediction_loss_only:
					return loss, None, None

			if has_labels:
					lowerCamelCase     :			Tuple		=    inputs["labels"]
					if labels.shape[-1] < gen_config.max_length:
							lowerCamelCase     :			Any		=    self._pad_tensors_to_max_len(snake_case__		, gen_config.max_length					)
					elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
							lowerCamelCase     :			Tuple		=    self._pad_tensors_to_max_len(snake_case__		, gen_config.max_new_tokens + 1					)
			else:
					lowerCamelCase     :			List[Any]		=    None

			return loss, generated_tokens, labels




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							str:
			if self.tokenizer is not None and hasattr(self.tokenizer		, 'pad_token_id'					):
					# If PAD token is not defined at least EOS token has to be defined
					lowerCamelCase     :			Optional[int]		=    (
					    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
					)
			else:
					if self.model.config.pad_token_id is not None:
							lowerCamelCase     :			int		=    self.model.config.pad_token_id
					else:
							raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'					)

			lowerCamelCase     :			List[Any]		=    pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)		, dtype=tensor.dtype		, device=tensor.device					)
			lowerCamelCase     :			str		=    tensor
			return padded_tensor














"""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        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"runwayml/stable-diffusion-v1-5"
        UpperCAmelCase__		:  Optional[Any]				=				TextToVideoZeroPipeline.from_pretrained(snake_case__				,       torch_dtype=torch.floataa				).to("cuda"				)
        UpperCAmelCase__		:  Any				=				DDIMScheduler.from_config(pipe.scheduler.config				)
        UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device="cuda"				).manual_seed(0				)

        UpperCAmelCase__		:  Any				=				"A bear is playing a guitar on Times Square"
        UpperCAmelCase__		:  Dict				=				pipe(prompt=snake_case__				,       generator=snake_case__				).images

        UpperCAmelCase__		:  List[Any]				=				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(snake_case__				,       snake_case__				)


