




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 lowerCamelCase       :	Tuple									=     "0.12"  # assumed parallelism: 8


if is_torch_available():
 import torch

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: List[str]			,							lowercase			: List[Any]=None  ):


      '''simple docstring'''



      if rng is None:
            lowerCamelCase_	  =					random.Random()

      lowerCamelCase_	  =					1
      for dim in shape:
            total_dims *= dim

      lowerCamelCase_	  =					[]
      for _ in range(lowercase  ):
            values.append(rng.randint(0			,							vocab_size - 1  )  )

      lowerCamelCase_	  =					np.array(lowercase			,							dtype=jnp.intaa  ).reshape(lowercase  )

      return output

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Tuple=None  ):


      '''simple docstring'''



      lowerCamelCase_	  =					ids_tensor(lowercase			,							vocab_size=2			,							rng=lowercase  )
      # make sure that at least one token is attended to for each batch
      lowerCamelCase_	  =					1
      return attn_mask







@require_flax
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		None
 UpperCamelCase		     =		()

 def        a__				(       self      :						Optional[int]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       lowerCamelCase_	  =					2
       lowerCamelCase_	  =					inputs['input_ids'].shape[-1] // 2
       lowerCamelCase_	  =					inputs['input_ids'][:max_batch_size, :sequence_length]

       lowerCamelCase_	  =					jnp.ones_like(A_						)
       lowerCamelCase_	  =					attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       lowerCamelCase_	  =					input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             lowerCamelCase_	  =					config.eos_token_id
       return config, input_ids, attention_mask, max_length

 @is_pt_flax_cross_test
 def        a__				(       self      :						Optional[Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					0

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase_	  =					getattr(A_		,							A_						)
             lowerCamelCase_	  =					pt_model_class(A_						).eval()
             lowerCamelCase_	  =					load_flax_weights_in_pytorch_model(A_		,							flax_model.params						)

             lowerCamelCase_	  =					flax_model.generate(A_						).sequences
             lowerCamelCase_	  =					pt_model.generate(torch.tensor(A_		,							dtype=torch.long						)						)

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   lowerCamelCase_	  =					flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist()		,							flax_generation_outputs.tolist()						)

 def        a__				(       self      :						List[Any]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					max_length

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					True
       lowerCamelCase_	  =					max_length

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					2

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

 def        a__				(       self      :						int						)     ->			int:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					2
       lowerCamelCase_	  =					2

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[0]		,							input_ids.shape[0] * config.num_return_sequences						)

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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					True
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					0.8
       lowerCamelCase_	  =					10
       lowerCamelCase_	  =					0.3
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					8
       lowerCamelCase_	  =					9

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					8
       lowerCamelCase_	  =					9

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

 def        a__				(       self      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()
       lowerCamelCase_	  =					max_length
       lowerCamelCase_	  =					2
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					8
       lowerCamelCase_	  =					9

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

 def        a__				(       self      :						Any						)     ->			str:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase_	  =					attention_mask.at[(0, 0)].set(0						)

       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					max_length

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_		,							attention_mask=A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_		,							attention_mask=A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)

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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase_	  =					attention_mask.at[(0, 0)].set(0						)

       lowerCamelCase_	  =					True
       lowerCamelCase_	  =					max_length

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_		,							attention_mask=A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_		,							attention_mask=A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)





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

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase_	  =					attention_mask.at[(0, 0)].set(0						)

       lowerCamelCase_	  =					2
       lowerCamelCase_	  =					max_length

       for model_class in self.all_generative_model_classes:
             lowerCamelCase_	  =					model_class(A_						)

             lowerCamelCase_	  =					model.generate(A_		,							attention_mask=A_						).sequences
             self.assertEqual(generation_outputs.shape[-1]		,							A_						)

             lowerCamelCase_	  =					jit(model.generate						)
             lowerCamelCase_	  =					jit_generate(A_		,							attention_mask=A_						).sequences

             self.assertListEqual(generation_outputs.tolist()		,							jit_generation_outputs.tolist()						)







@require_flax
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'						)
       lowerCamelCase_	  =					FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'						)

       lowerCamelCase_	  =					'Hello world'
       lowerCamelCase_	  =					tokenizer(A_		,							return_tensors='np'						).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(A_		,							'do_samples'						):
             model.generate(A_		,							do_samples=A_						)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(A_		,							'foo'						):
             lowerCamelCase_	  =					{'foo': 'bar'}
             model.generate(A_		,							**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						)



