


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image
class 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Dict=2					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=False					,							SCREAMING_SNAKE_CASE__    :	int=1_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=3					,							SCREAMING_SNAKE_CASE__    :	List[Any]=3_2 * 8					,							SCREAMING_SNAKE_CASE__    :	str=3_2 * 8					,							SCREAMING_SNAKE_CASE__    :	int=4					,							SCREAMING_SNAKE_CASE__    :	List[Any]=6_4					,							)		->      Any:
             a_ :		Union[str, Any]							=  parent
             a_ :		int							=  batch_size
             a_ :		Optional[Any]							=  is_training
             a_ :		Any							=  use_auxiliary_loss
             a_ :		List[str]							=  num_queries
             a_ :		Union[str, Any]							=  num_channels
             a_ :		List[str]							=  min_size
             a_ :		List[Any]							=  max_size
             a_ :		Optional[int]							=  num_labels
             a_ :		int							=  hidden_dim
             a_ :		int							=  hidden_dim
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Optional[Any]:
             a_ :		Tuple							=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
                 SCREAMING_SNAKE_CASE__					)

             a_ :		str							=  torch.ones([self.batch_size, self.min_size, self.max_size]					,							device=SCREAMING_SNAKE_CASE__					)

             a_ :		List[str]							=  (
                 torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,							device=SCREAMING_SNAKE_CASE__					) > 0.5
             ).float()
             a_ :		Dict							=  (torch.rand((self.batch_size, self.num_labels)					,							device=SCREAMING_SNAKE_CASE__					) > 0.5).long()

             a_ :		Dict							=  self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Tuple:
             a_ :		List[Any]							=  MaskaFormerConfig(
                 hidden_size=self.hidden_dim					,							)
             a_ :		Union[str, Any]							=  self.num_queries
             a_ :		Any							=  self.num_labels

             a_ :		int							=  [1, 1, 1, 1]
             a_ :		Union[str, Any]							=  self.num_channels

             a_ :		Union[str, Any]							=  6_4
             a_ :		Dict							=  1_2_8
             a_ :		Any							=  self.hidden_dim
             a_ :		int							=  self.hidden_dim
             a_ :		int							=  self.hidden_dim
             return config
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Optional[Any]:
             a_							,		a_							,		a_							,		a_							,		a_ :		List[str]							=  self.prepare_config_and_inputs()
             a_ :		List[str]							=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
             return config, inputs_dict
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Optional[Any]:
             a_ :		List[str]							=  output.encoder_hidden_states
             a_ :		List[Any]							=  output.pixel_decoder_hidden_states
             a_ :		int							=  output.transformer_decoder_hidden_states

             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							len(config.backbone_config.depths					)					)
             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							len(config.backbone_config.depths					)					)
             self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__					)					,							config.decoder_layers					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Dict=False					)		->      Optional[int]:
             with torch.no_grad():
                    a_ :		Any							=  MaskaFormerModel(config=SCREAMING_SNAKE_CASE__					)
                    model.to(SCREAMING_SNAKE_CASE__					)
                    model.eval()

                    a_ :		Union[str, Any]							=  model(pixel_values=SCREAMING_SNAKE_CASE__					,							pixel_mask=SCREAMING_SNAKE_CASE__					)
                    a_ :		Optional[int]							=  model(SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_SNAKE_CASE__					)

             self.parent.assertEqual(
                 output.transformer_decoder_last_hidden_state.shape					,							(self.batch_size, self.num_queries, self.hidden_dim)					,							)
             # let's ensure the other two hidden state exists
             self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
             self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

             if output_hidden_states:
                    self.check_output_hidden_state(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					)		->      Optional[int]:
             a_ :		List[str]							=  MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__					)
             model.to(SCREAMING_SNAKE_CASE__					)
             model.eval()

             def comm_check_on_output(SCREAMING_SNAKE_CASE__    :	Union[str, Any]					):
                    # let's still check that all the required stuff is there
                    self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
                    self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
                    self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
                    # okay, now we need to check the logits shape
                    # due to the encoder compression, masks have a //4 spatial size
                    self.parent.assertEqual(
                        result.masks_queries_logits.shape					,							(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)					,							)
                    # + 1 for null class
                    self.parent.assertEqual(
                        result.class_queries_logits.shape					,							(self.batch_size, self.num_queries, self.num_labels + 1)					)

             with torch.no_grad():
                    a_ :		List[str]							=  model(pixel_values=SCREAMING_SNAKE_CASE__					,							pixel_mask=SCREAMING_SNAKE_CASE__					)
                    a_ :		List[Any]							=  model(SCREAMING_SNAKE_CASE__					)

                    comm_check_on_output(SCREAMING_SNAKE_CASE__					)

                    a_ :		Optional[int]							=  model(
                        pixel_values=SCREAMING_SNAKE_CASE__					,							pixel_mask=SCREAMING_SNAKE_CASE__					,							mask_labels=SCREAMING_SNAKE_CASE__					,							class_labels=SCREAMING_SNAKE_CASE__					)

             comm_check_on_output(SCREAMING_SNAKE_CASE__					)

             self.parent.assertTrue(result.loss is not None					)
             self.parent.assertEqual(result.loss.shape					,							torch.Size([1]					)					)
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Optional[int]					       =						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
      snake_case__    :					Optional[Any]					       =						{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

      snake_case__    :					List[Any]					       =						False
      snake_case__    :					Any					       =						False
      snake_case__    :					Tuple					       =						False
      snake_case__    :					Tuple					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      int:
             a_ :		List[Any]							=  MaskaFormerModelTester(self					)
             a_ :		Union[str, Any]							=  ConfigTester(self					,							config_class=SCREAMING_SNAKE_CASE__					,							has_text_modality=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[Any]:
             self.config_tester.run_common_tests()
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[Any]:
             a_							,		a_ :		Any							=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[Any]:
             a_ :		Tuple							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__					)
      @unittest.skip(reason='Mask2Former does not use inputs_embeds'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Any:
             pass
      @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Tuple:
             pass
      @unittest.skip(reason='Mask2Former is not a generative model'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[int]:
             pass
      @unittest.skip(reason='Mask2Former does not use token embeddings'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      int:
             pass
      @require_torch_multi_gpu
      @unittest.skip(
          reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             pass
      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Tuple:
             pass
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
             a_							,		a_ :		Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_ :		str							=  model_class(SCREAMING_SNAKE_CASE__					)
                    a_ :		List[str]							=  inspect.signature(model.forward					)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    a_ :		Tuple							=  [*signature.parameters.keys()]

                    a_ :		List[str]							=  ['pixel_values']
                    self.assertListEqual(arg_names[:1]					,							SCREAMING_SNAKE_CASE__					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      str:
             for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                    a_ :		List[str]							=  MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[Any]:
             a_ :		Union[str, Any]							=  (self.model_tester.min_size,) * 2
             a_ :		Dict							=  {
                 'pixel_values': torch.randn((2, 3, *size)					,							device=SCREAMING_SNAKE_CASE__					),
                 'mask_labels': torch.randn((2, 1_0, *size)					,							device=SCREAMING_SNAKE_CASE__					),
                 'class_labels': torch.zeros(2					,							1_0					,							device=SCREAMING_SNAKE_CASE__					).long(),
             }
             a_ :		Optional[Any]							=  self.model_tester.get_config()

             a_ :		Optional[int]							=  MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  model(**SCREAMING_SNAKE_CASE__					)
             self.assertTrue(outputs.loss is not None					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_							,		a_ :		Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					,							output_hidden_states=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      int:
             a_							,		a_ :		Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    a_ :		Union[str, Any]							=  model_class(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
                    a_ :		Optional[int]							=  model(**SCREAMING_SNAKE_CASE__					,							output_attentions=SCREAMING_SNAKE_CASE__					)
                    self.assertTrue(outputs.attentions is not None					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
             if not self.model_tester.is_training:
                    return

             a_ :		str							=  self.all_model_classes[1]
             a_							,		a_							,		a_							,		a_							,		a_ :		Dict							=  self.model_tester.prepare_config_and_inputs()

             a_ :		Optional[int]							=  model_class(SCREAMING_SNAKE_CASE__					)
             model.to(SCREAMING_SNAKE_CASE__					)
             model.train()

             a_ :		List[Any]							=  model(SCREAMING_SNAKE_CASE__					,							mask_labels=SCREAMING_SNAKE_CASE__					,							class_labels=SCREAMING_SNAKE_CASE__					).loss
             loss.backward()

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Dict							=  self.all_model_classes[1]
             a_							,		a_							,		a_							,		a_							,		a_ :		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             a_ :		Dict							=  True
             a_ :		Optional[Any]							=  True

             a_ :		str							=  model_class(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             model.train()

             a_ :		Tuple							=  model(SCREAMING_SNAKE_CASE__					,							mask_labels=SCREAMING_SNAKE_CASE__					,							class_labels=SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

             a_ :		List[str]							=  outputs.pixel_decoder_hidden_states[0]
             pixel_decoder_hidden_states.retain_grad()

             a_ :		List[Any]							=  outputs.transformer_decoder_hidden_states[0]
             transformer_decoder_hidden_states.retain_grad()

             a_ :		List[Any]							=  outputs.attentions[0]
             attentions.retain_grad()

             outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__					)

             self.assertIsNotNone(encoder_hidden_states.grad					)
             self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
             self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
             self.assertIsNotNone(attentions.grad					)
UpperCAmelCase_				:			Optional[int]   =	1e-4







def 	SCREAMING_SNAKE_CASE_						(      )     ->		str:

       """simple docstring"""

       a_ :		Any							=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
       return image
@require_vision
@slow
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @cached_property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Optional[Any]:
             return "facebook/mask2former-swin-small-coco-instance"
      @cached_property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[Any]:
             return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints					) if is_vision_available() else None
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      str:
             a_ :		List[str]							=  MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  self.default_image_processor
             a_ :		List[Any]							=  prepare_img()
             a_ :		Optional[int]							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  inputs['pixel_values'].shape
             # check size is divisible by 32
             self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0					)
             # check size
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							(1, 3, 3_8_4, 3_8_4)					)

             with torch.no_grad():
                    a_ :		int							=  model(**SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  torch.tensor(
                 [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.encoder_last_hidden_state[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

             a_ :		Union[str, Any]							=  torch.tensor(
                 [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

             a_ :		List[Any]							=  torch.tensor(
                 [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(
                 torch.allclose(
                     outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Dict:
             a_ :		int							=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(SCREAMING_SNAKE_CASE__					).eval()
             a_ :		List[Any]							=  self.default_image_processor
             a_ :		int							=  prepare_img()
             a_ :		Optional[int]							=  image_processor(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  inputs['pixel_values'].shape
             # check size is divisible by 32
             self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0					)
             # check size
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							(1, 3, 3_8_4, 3_8_4)					)

             with torch.no_grad():
                    a_ :		Optional[int]							=  model(**SCREAMING_SNAKE_CASE__					)
             # masks_queries_logits
             a_ :		List[str]							=  outputs.masks_queries_logits
             self.assertEqual(
                 masks_queries_logits.shape					,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
             a_ :		List[str]							=  [
                 [-8.7839, -9.0056, -8.8121],
                 [-7.4104, -7.0313, -6.5401],
                 [-6.6105, -6.3427, -6.4675],
             ]
             a_ :		Optional[Any]							=  torch.tensor(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)
             # class_queries_logits
             a_ :		Union[str, Any]							=  outputs.class_queries_logits
             self.assertEqual(class_queries_logits.shape					,							(1, model.config.num_queries, model.config.num_labels + 1)					)
             a_ :		Optional[Any]							=  torch.tensor(
                 [
                     [1.8324, -8.0835, -4.1922],
                     [0.8450, -9.0050, -3.6053],
                     [0.3045, -7.7293, -3.0275],
                 ]					).to(SCREAMING_SNAKE_CASE__					)
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,							SCREAMING_SNAKE_CASE__					,							atol=SCREAMING_SNAKE_CASE__					)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             a_ :		Tuple							=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(SCREAMING_SNAKE_CASE__					).eval()
             a_ :		Any							=  self.default_image_processor

             a_ :		Dict							=  image_processor(
                 [np.zeros((3, 8_0_0, 1_3_3_3)					), np.zeros((3, 8_0_0, 1_3_3_3)					)]					,							segmentation_maps=[np.zeros((3_8_4, 3_8_4)					).astype(np.floataa					), np.zeros((3_8_4, 3_8_4)					).astype(np.floataa					)]					,							return_tensors='pt'					,							)

             a_ :		int							=  inputs['pixel_values'].to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  [el.to(SCREAMING_SNAKE_CASE__					) for el in inputs['mask_labels']]
             a_ :		List[str]							=  [el.to(SCREAMING_SNAKE_CASE__					) for el in inputs['class_labels']]

             with torch.no_grad():
                    a_ :		Any							=  model(**SCREAMING_SNAKE_CASE__					)

             self.assertTrue(outputs.loss is not None					)






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


UpperCAmelCase_				:			str   =	[
    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)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  os.path.join(__A  ,   F"""{split}_results.json"""    )
       if os.path.exists(__A    ):
              with open(__A  ,   'r'    ) as f:
                     return json.load(__A    )
       raise ValueError(F"""can't find {path}"""    )


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		int							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


