

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_MAPPING,
           DataaVecVisionForImageClassification,
           DataaVecVisionForSemanticSegmentation,
           DataaVecVisionModel,
       )
       from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a		,     a=100		,     a=13		,     a=30		,     a=2		,     a=3		,     a=True		,     a=True		,     a=32		,     a=4		,     a=4		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=10		,     a=0.02		,     a=3		,     a=None		,     a=[0, 1, 2, 3]		,     ):
         lowercase__					:     List[Any]   = parent
         lowercase__					:     Any   = 100
         lowercase__					:     str   = batch_size
         lowercase__					:     Optional[int]   = image_size
         lowercase__					:     Tuple   = patch_size
         lowercase__					:     List[str]   = num_channels
         lowercase__					:     Tuple   = is_training
         lowercase__					:     int   = use_labels
         lowercase__					:     Any   = hidden_size
         lowercase__					:     Tuple   = num_hidden_layers
         lowercase__					:     Optional[Any]   = num_attention_heads
         lowercase__					:     int   = intermediate_size
         lowercase__					:     int   = hidden_act
         lowercase__					:     Dict   = hidden_dropout_prob
         lowercase__					:     List[str]   = attention_probs_dropout_prob
         lowercase__					:     List[Any]   = type_sequence_label_size
         lowercase__					:     Any   = initializer_range
         lowercase__					:     Any   = scope
         lowercase__					:     List[Any]   = out_indices
         lowercase__					:     Union[str, Any]   = num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         lowercase__					:     str   = (image_size // patch_size) ** 2
         lowercase__					:     Dict   = num_patches + 1





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__					:     str   = None
         lowercase__					:     Dict   = None
         if self.use_labels:
              lowercase__					:     Optional[Any]   = ids_tensor([self.batch_size]		,     self.type_sequence_label_size)
              lowercase__					:     Any   = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels)

         lowercase__					:     str   = self.get_config()

         return config, pixel_values, labels, pixel_labels





    def    snake_case_       (   self):
         return DataaVecVisionConfig(
             vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=a		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )





    def    snake_case_       (   self		,     a		,     a		,     a		,     a):
         lowercase__					:     str   = DataaVecVisionModel(config=a)
         model.to(a)
         model.eval()
         lowercase__					:     List[Any]   = model(a)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         lowercase__					:     Optional[int]   = (self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, num_patches + 1, self.hidden_size))





    def    snake_case_       (   self		,     a		,     a		,     a		,     a):
         lowercase__					:     Tuple   = self.type_sequence_label_size
         lowercase__					:     List[str]   = DataaVecVisionForImageClassification(a)
         model.to(a)
         model.eval()
         lowercase__					:     Union[str, Any]   = model(a		,     labels=a)
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size))





    def    snake_case_       (   self		,     a		,     a		,     a		,     a):
         lowercase__					:     Optional[Any]   = self.num_labels
         lowercase__					:     Optional[int]   = DataaVecVisionForSemanticSegmentation(a)
         model.to(a)
         model.eval()
         lowercase__					:     Union[str, Any]   = model(a)
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
         lowercase__					:     int   = model(a		,     labels=a)
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = self.prepare_config_and_inputs()
         lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     Dict   = config_and_inputs
         lowercase__					:     Any   = {'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: Optional[Any]														=  (
        (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    __lowerCamelCase		: Tuple														=  (
        {
            """feature-extraction""": DataaVecVisionModel,
            """image-classification""": DataaVecVisionForImageClassification,
            """image-segmentation""": DataaVecVisionForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __lowerCamelCase		: List[str]														=  False
    __lowerCamelCase		: int														=  False
    __lowerCamelCase		: List[str]														=  False





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = DataaVecVisionModelTester(self)
         lowercase__					:     Tuple   = ConfigTester(
             self		,     config_class=a		,     has_text_modality=a		,     hidden_size=37)





    def    snake_case_       (   self):
         self.config_tester.run_common_tests()





    def    snake_case_       (   self):
         # Data2VecVision does not use inputs_embeds
         pass





    @require_torch_multi_gpu
    @unittest.skip(
        reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
    def    snake_case_       (   self):
         pass





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Any   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     Dict   = model_class(a)
              self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module))
              lowercase__					:     str   = model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(a		,     nn.Linear))





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Union[str, Any]   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     int   = model_class(a)
              lowercase__					:     Any   = inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase__					:     Any   = [*signature.parameters.keys()]

              lowercase__					:     Optional[Any]   = ['pixel_values']
              self.assertListEqual(arg_names[:1]		,     a)





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    def    snake_case_       (   self):
         lowercase__					:     Any   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*a)





    def    snake_case_       (   self):
         if not self.model_tester.is_training:
              return

         lowercase__   ,				lowercase__					:     Dict   = self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__					:     Any   = True

         for model_class in self.all_model_classes:
              if model_class in [*get_values(a)]:
                   continue

              lowercase__					:     List[str]   = model_class(a)
              model.to(a)
              model.train()
              lowercase__					:     Any   = self._prepare_for_class(a		,     a		,     return_labels=a)
              lowercase__					:     Optional[int]   = model(**a).loss
              loss.backward()





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         lowercase__					:     str   = False
         lowercase__					:     List[Any]   = True

         for model_class in self.all_model_classes:
              if model_class in [*get_values(a)] or not model_class.supports_gradient_checkpointing:
                   continue
              # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
              # this can then be incorporated into _prepare_for_class in test_modeling_common.py
              elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                   lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     int   = inputs_dict['pixel_values'].shape
                   lowercase__					:     Any   = torch.zeros(
                       [self.model_tester.batch_size, height, width]		,     device=a).long()
              lowercase__					:     Tuple   = model_class(a)
              model.gradient_checkpointing_enable()
              model.to(a)
              model.train()
              lowercase__					:     Union[str, Any]   = self._prepare_for_class(a		,     a		,     return_labels=a)
              lowercase__					:     Optional[int]   = model(**a).loss
              loss.backward()





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     List[Any]   = self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__					:     List[Any]   = _config_zero_init(a)
         for model_class in self.all_model_classes:
              lowercase__					:     str   = model_class(config=a)
              for name, param in model.named_parameters():
                   # we skip lambda parameters as these require special initial values
                   # determined by config.layer_scale_init_value
                   if "lambda" in name:
                        continue
                   if param.requires_grad:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )





    def    snake_case_       (   self		,     a		,     a		,     a		,     a=2e-4		,     a="outputs"		,     a=None):
         # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
         super().check_pt_tf_outputs(a		,     a		,     a		,     a		,     a		,     a)





    def    snake_case_       (   self):
         lowercase__					:     Union[str, Any]   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a)





    @slow
    def    snake_case_       (   self):
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__					:     List[Any]   = DataaVecVisionModel.from_pretrained(a)
              self.assertIsNotNone(a)



def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     Optional[int]   = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
     return image


@require_torch
@require_vision
class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    @cached_property
    def    snake_case_       (   self):
         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k') if is_vision_available() else None
         )





    @slow
    def    snake_case_       (   self):
         lowercase__					:     Dict   = DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k').to(
             a)

         lowercase__					:     Optional[Any]   = self.default_image_processor
         lowercase__					:     Optional[int]   = prepare_img()
         lowercase__					:     List[Any]   = image_processor(images=a		,     return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__					:     Dict   = model(**a)
         lowercase__					:     List[Any]   = outputs.logits

         # verify the logits
         lowercase__					:     Any   = torch.Size((1, 1000))
         self.assertEqual(logits.shape		,     a)

         lowercase__					:     Union[str, Any]   = torch.tensor([0.3_277, -0.1_395, 0.0_911]).to(a)

         self.assertTrue(torch.allclose(logits[0, :3]		,     a		,     atol=1e-4))

         lowercase__					:     Tuple   = [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2).indices.cpu().tolist()		,     a)




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    @classmethod
    def    snake_case_       (   cls):
         return cls





    @slow
    @require_torch_gpu
    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
         lowercase__					:     List[str]   = f"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

         # we want this test to run even if there is only one GPU, but if there are more we use them all
         lowercase__					:     int   = get_gpu_count()
         lowercase__					:     Tuple   = f"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
         lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
         execute_subprocess_async(a		,     env=self.get_env())

         lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
         lowercase__					:     List[str]   = load_json(a)
         # print(metrics)
         self.assertGreaterEqual(metrics['bleu']		,     25)

