




'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCamelCase       :	Dict									=     logging.get_logger(__name__)  # pylint: disable=invalid-name







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						Dict		,							A_      :						Dict		,							A_      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       super().__init__()
       self.register_modules(unet=A_		,							scheduler=A_						)





 @torch.no_grad()
 def __call__(       self      :						int		,							A_      :						int = 1		,							A_      :						int = 100		,							A_      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,							A_      :						Optional[float] = None		,							A_      :						bool = True		,							)     ->			Union[AudioPipelineOutput, Tuple]:

       """simple docstring"""






       if audio_length_in_s is None:
             lowerCamelCase_	  =					self.unet.config.sample_size / self.unet.config.sample_rate

       lowerCamelCase_	  =					audio_length_in_s * self.unet.config.sample_rate

       lowerCamelCase_	  =					2 ** len(self.unet.up_blocks						)
       if sample_size < 3 * down_scale_factor:
             raise ValueError(
                 f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                 f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""						)

       lowerCamelCase_	  =					int(A_						)
       if sample_size % down_scale_factor != 0:
             lowerCamelCase_	  =					(
                 (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
             ) * down_scale_factor
             logger.info(
                 f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                 f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                 ' process.'						)
       lowerCamelCase_	  =					int(A_						)

       lowerCamelCase_	  =					next(iter(self.unet.parameters()						)						).dtype
       lowerCamelCase_	  =					(batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_		,							A_						) and len(A_						) != batch_size:
             raise ValueError(
                 f"""You have passed a list of generators of length {len(A_						)}, but requested an effective batch"""
                 f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

       lowerCamelCase_	  =					randn_tensor(A_		,							generator=A_		,							device=self.device		,							dtype=A_						)

       # set step values
       self.scheduler.set_timesteps(A_		,							device=audio.device						)
       lowerCamelCase_	  =					self.scheduler.timesteps.to(A_						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # 1. predict noise model_output
             lowerCamelCase_	  =					self.unet(A_		,							A_						).sample

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

       lowerCamelCase_	  =					audio.clamp(-1		,							1						).float().cpu().numpy()

       lowerCamelCase_	  =					audio[:, :, :original_sample_size]

       if not return_dict:
             return (audio,)

       return AudioPipelineOutput(audios=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						)



