

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


a_    		=      [
    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)

a_    		=      logging.getLogger()


def        __lowerCAmelCase    (       )		->							int:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Dict="eval"	)		->							Optional[Any]:
 '''simple docstring'''


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


a_    		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_flax_glue.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertGreaterEqual(result['eval_accuracy']							,    0.75    )

    @slow
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_clm_flax.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertLess(result['eval_perplexity']							,    1_0_0    )

    @slow
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_summarization_flax.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__							,    split='test'    )
      self.assertGreaterEqual(result['test_rouge1']							,    1_0    )
      self.assertGreaterEqual(result['test_rouge2']							,    2    )
      self.assertGreaterEqual(result['test_rougeL']							,    7    )
      self.assertGreaterEqual(result['test_rougeLsum']							,    7    )

    @slow
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_mlm_flax.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertLess(result['eval_perplexity']							,    4_2    )

    @slow
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_ta_mlm_flax.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertGreaterEqual(result['eval_accuracy']							,    0.42    )

    @slow
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_flax_ner.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertGreaterEqual(result['eval_accuracy']							,    0.75    )
      self.assertGreaterEqual(result['eval_f1']							,    0.3    )




    @slow
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			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(snake_case__							,    'argv'							,    snake_case__    ):
      run_qa.main()
      SCREAMING_SNAKE_CASE								=			get_results(snake_case__    )
      self.assertGreaterEqual(result['eval_f1']							,    3_0    )
      self.assertGreaterEqual(result['eval_exact']							,    3_0    )




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

