




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						List[str]		,							A_      :						TransformeraDModel		,							A_      :						AutoencoderKL		,							A_      :						KarrasDiffusionSchedulers		,							A_      :						Optional[Dict[int, str]] = None		,							)     ->			Tuple:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_		,							vae=A_		,							scheduler=A_						)

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_	  =					{}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','						):
                         lowerCamelCase_	  =					int(A_						)
             lowerCamelCase_	  =					dict(sorted(self.labels.items()						)						)

 def        a__				(       self      :						Dict		,							A_      :						Union[str, List[str]]						)     ->			List[int]:

       """simple docstring"""






       if not isinstance(A_		,							A_						):
             lowerCamelCase_	  =					list(A_						)

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""						)

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(       self      :						int		,							A_      :						List[int]		,							A_      :						float = 4.0		,							A_      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,							A_      :						int = 50		,							A_      :						Optional[str] = "pil"		,							A_      :						bool = True		,							)     ->			Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_	  =					len(A_						)
       lowerCamelCase_	  =					self.transformer.config.sample_size
       lowerCamelCase_	  =					self.transformer.config.in_channels

       lowerCamelCase_	  =					randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)		,							generator=A_		,							device=self.device		,							dtype=self.transformer.dtype		,							)
       lowerCamelCase_	  =					torch.cat([latents] * 2						) if guidance_scale > 1 else latents

       lowerCamelCase_	  =					torch.tensor(A_		,							device=self.device						).reshape(-1						)
       lowerCamelCase_	  =					torch.tensor([1000] * batch_size		,							device=self.device						)
       lowerCamelCase_	  =					torch.cat([class_labels, class_null]		,							0						) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             if guidance_scale > 1:
                   lowerCamelCase_	  =					latent_model_input[: len(A_						) // 2]
                   lowerCamelCase_	  =					torch.cat([half, half]		,							dim=0						)
             lowerCamelCase_	  =					self.scheduler.scale_model_input(A_		,							A_						)

             lowerCamelCase_	  =					t
             if not torch.is_tensor(A_						):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_	  =					latent_model_input.device.type == 'mps'
                   if isinstance(A_		,							A_						):
                         lowerCamelCase_	  =					torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_	  =					torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_	  =					torch.tensor([timesteps]		,							dtype=A_		,							device=latent_model_input.device						)
             elif len(timesteps.shape						) == 0:
                   lowerCamelCase_	  =					timesteps[None].to(latent_model_input.device						)
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_	  =					timesteps.expand(latent_model_input.shape[0]						)
             # predict noise model_output
             lowerCamelCase_	  =					self.transformer(
                 A_		,							timestep=A_		,							class_labels=A_						).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_  ,       lowerCamelCase_	  =					torch.split(A_		,							len(A_						) // 2		,							dim=0						)

                   lowerCamelCase_	  =					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_	  =					torch.cat([half_eps, half_eps]		,							dim=0						)

                   lowerCamelCase_	  =					torch.cat([eps, rest]		,							dim=1						)

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_  ,       lowerCamelCase_	  =					torch.split(A_		,							A_		,							dim=1						)
             else:
                   lowerCamelCase_	  =					noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_	  =					self.scheduler.step(A_		,							A_		,							A_						).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_  ,       lowerCamelCase_	  =					latent_model_input.chunk(2		,							dim=0						)
       else:
             lowerCamelCase_	  =					latent_model_input

       lowerCamelCase_	  =					1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_	  =					self.vae.decode(A_						).sample

       lowerCamelCase_	  =					(samples / 2 + 0.5).clamp(0		,							1						)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_	  =					samples.cpu().permute(0		,							2		,							3		,							1						).float().numpy()

       if output_type == "pil":
             lowerCamelCase_	  =					self.numpy_to_pil(A_						)

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_						)









import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCamelCase       :	List[Any]									=     [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
 import run_clm_flax
 import run_flax_glue
 import run_flax_ner
 import run_mlm_flax
 import run_qa
 import run_summarization_flax
 import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase       :	Tuple									=     logging.getLogger()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					argparse.ArgumentParser()
      parser.add_argument('-f'  )
      lowerCamelCase_	  =					parser.parse_args()
      return args.f

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Dict="eval"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					os.path.join(lowercase			,							f"""{split}_results.json"""  )
      if os.path.exists(lowercase  ):
            with open(lowercase			,							'r'  ) as f:
                  return json.load(lowercase  )
      raise ValueError(f"""can't find {path}"""  )


lowerCamelCase       :	str									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_glue.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)

 @slow
 def        a__				(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_clm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							100						)

 @slow
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_summarization_flax.main()
             lowerCamelCase_	  =					get_results(A_		,							split='test'						)
             self.assertGreaterEqual(result['test_rouge1']		,							10						)
             self.assertGreaterEqual(result['test_rouge2']		,							2						)
             self.assertGreaterEqual(result['test_rougeL']		,							7						)
             self.assertGreaterEqual(result['test_rougeLsum']		,							7						)

 @slow
 def        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							42						)

 @slow
 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_ta_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.42						)

 @slow
 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					7 if get_gpu_count() > 1 else 2

       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_ner.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)
             self.assertGreaterEqual(result['eval_f1']		,							0.3						)





 @slow
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_qa.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_f1']		,							30						)
             self.assertGreaterEqual(result['eval_exact']		,							30						)



