


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


SCREAMING_SNAKE_CASE		:int     						=   logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			__lowerCAmelCase   (       snake_case_    ):






  """simple docstring"""





  def __init__(   self				:				Tuple			, _lowerCAmelCase				:				Any			, _lowerCAmelCase				:				Optional[Any]=7_6_8     )      ->       Optional[int]:




     """simple docstring"""
     super().__init__(_lowerCAmelCase     )
     snake_case_    			=    proj_size

     snake_case_    			=    CLIPVisionModel(_lowerCAmelCase     )
     snake_case_    			=    PaintByExampleMapper(_lowerCAmelCase     )
     snake_case_    			=    nn.LayerNorm(config.hidden_size     )
     snake_case_    			=    nn.Linear(config.hidden_size			, self.proj_size     )

     # uncondition for scaling
     snake_case_    			=    nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )



  def     lowerCAmelCase__     (   self				:				Optional[int]			, _lowerCAmelCase				:				Any			, _lowerCAmelCase				:				Optional[Any]=False     )      ->       List[Any]:




     """simple docstring"""
     snake_case_    			=    self.model(pixel_values=_lowerCAmelCase     )
     snake_case_    			=    clip_output.pooler_output
     snake_case_    			=    self.mapper(latent_states[:, None]     )
     snake_case_    			=    self.final_layer_norm(_lowerCAmelCase     )
     snake_case_    			=    self.proj_out(_lowerCAmelCase     )
     if return_uncond_vector:
        return latent_states, self.uncond_vector

     return latent_states






class 			__lowerCAmelCase   (       nn.Module    ):






  """simple docstring"""





  def __init__(   self				:				Any			, _lowerCAmelCase				:				Union[str, Any]     )      ->       Any:




     """simple docstring"""
     super().__init__()
     snake_case_    			=    (config.num_hidden_layers + 1) // 5
     snake_case_    			=    config.hidden_size
     snake_case_    			=    1
     snake_case_    			=    nn.ModuleList(
         [
             BasicTransformerBlock(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, activation_fn="gelu"			, attention_bias=_lowerCAmelCase     )
             for _ in range(_lowerCAmelCase     )
         ]     )



  def     lowerCAmelCase__     (   self				:				Union[str, Any]			, _lowerCAmelCase				:				Any     )      ->       Tuple:




     """simple docstring"""
     for block in self.blocks:
        snake_case_    			=    block(_lowerCAmelCase     )

     return hidden_states






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  *lowercase						,  lowercase=None						,  lowercase=None						,  **lowercase )				->       List[str]:
					super().__init__(*lowercase						,  **lowercase )
					lowerCamelCase_						=			eval_examples
					lowerCamelCase_						=			post_process_function



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = None						,  lowercase=None						,  lowercase = None						,  lowercase = "eval"						,  **lowercase						,  )				->       Dict[str, float]:
					lowerCamelCase_						=			gen_kwargs.copy()
					lowerCamelCase_						=			(
					    gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
					)
					lowerCamelCase_						=			(
					    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
					)
					lowerCamelCase_						=			gen_kwargs

					lowerCamelCase_						=			self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCamelCase_						=			self.get_eval_dataloader(lowercase )
					lowerCamelCase_						=			self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Evaluation"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics
					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )

					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
									# Only the main node write the results by default
									lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase )
									lowerCamelCase_						=			self.compute_metrics(lowercase )

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys() ):
													if not key.startswith(f'{metric_key_prefix}_' ):
																	lowerCamelCase_						=			metrics.pop(lowercase )

									metrics.update(output.metrics )
					else:
									lowerCamelCase_						=			output.metrics

					if self.args.should_log:
									# Only the main node log the results by default
									self.log(lowercase )

					if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report() )

					lowerCamelCase_						=			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase )
					return metrics



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=None						,  lowercase = "test"						,  **lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			gen_kwargs.copy()

					lowerCamelCase_						=			self.get_test_dataloader(lowercase )

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Prediction"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics

					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )
					if self.post_process_function is None or self.compute_metrics is None:
									return output

					lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase						,  "predict" )
					lowerCamelCase_						=			self.compute_metrics(lowercase )

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys() ):
									if not key.startswith(f'{metric_key_prefix}_' ):
													lowerCamelCase_						=			metrics.pop(lowercase )
					metrics.update(output.metrics )
					return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase )


