


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=3      , __snake_case=True      , __snake_case=True      , __snake_case=0.1      , __snake_case=0.1      , __snake_case=2_2_4      , __snake_case=1_0_0_0      , __snake_case=[3, 3, 6, 4]      , __snake_case=[4_8, 5_6, 1_1_2, 2_2_0]      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    num_channels
         snake_case					=    is_training
         snake_case					=    use_labels
         snake_case					=    hidden_dropout_prob
         snake_case					=    attention_probs_dropout_prob
         snake_case					=    num_labels
         snake_case					=    image_size
         snake_case					=    layer_depths
         snake_case					=    embed_dims
    def 		a_      (	self   ):
         snake_case					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

         snake_case					=    None
         if self.use_labels:
              snake_case					=    ids_tensor([self.batch_size]      , self.num_labels   )

         snake_case					=    self.get_config()

         return config, pixel_values, labels
    def 		a_      (	self   ):
         return SwiftFormerConfig(
             depths=self.layer_depths      , embed_dims=self.embed_dims      , mlp_ratio=4      , downsamples=[True, True, True, True]      , hidden_act='''gelu'''      , num_labels=self.num_labels      , down_patch_size=3      , down_stride=2      , down_pad=1      , drop_rate=0.0      , drop_path_rate=0.0      , use_layer_scale=__snake_case      , layer_scale_init_value=1E-5      , )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    SwiftFormerModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.embed_dims[-1], 7, 7)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    self.num_labels
         snake_case					=    SwiftFormerForImageClassification(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )

         snake_case					=    SwiftFormerForImageClassification(__snake_case   )
         model.to(__snake_case   )
         model.eval()

         snake_case					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)   )




    def 		a_      (	self   ):
         ((snake_case)			,  (snake_case)			,  (snake_case))					=    self.prepare_config_and_inputs()
         snake_case					=    {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __magic_name__        =		(
        {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    SwiftFormerModelTester(self   )
         snake_case					=    ConfigTester(
             self      , config_class=__snake_case      , has_text_modality=__snake_case      , hidden_size=3_7      , num_attention_heads=1_2      , num_hidden_layers=1_2      , )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case					=    model_class(__snake_case   )
              snake_case					=    model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__snake_case      , nn.Linear   )   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case					=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case   )
    @slow
    def 		a_      (	self   ):
         for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case					=    SwiftFormerModel.from_pretrained(__snake_case   )
              self.assertIsNotNone(__snake_case   )
    @unittest.skip(reason='''SwiftFormer does not output attentions'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         def check_hidden_states_output(__snake_case      , __snake_case      , __snake_case   ):
              snake_case					=    model_class(__snake_case   )
              model.to(__snake_case   )
              model.eval()

              with torch.no_grad():
                   snake_case					=    model(**self._prepare_for_class(__snake_case      , __snake_case   )   )

              snake_case					=    outputs.hidden_states

              snake_case					=    8
              self.assertEqual(len(__snake_case   )      , __snake_case   )  # TODO

              # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
              # with the width and height being successively divided by 2, after every 2 blocks
              for i in range(len(__snake_case   )   ):
                   self.assertEqual(
                       hidden_states[i].shape      , torch.Size(
                           [
                               self.model_tester.batch_size,
                               self.model_tester.embed_dims[i // 2],
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           ]   )      , )

         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case					=    True
              check_hidden_states_output(__snake_case      , __snake_case      , __snake_case   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case					=    True

              check_hidden_states_output(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         def _config_zero_init(__snake_case   ):
              snake_case					=    copy.deepcopy(__snake_case   )
              for key in configs_no_init.__dict__.keys():
                   if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                        setattr(__snake_case      , __snake_case      , 1E-10   )
                   if isinstance(getattr(__snake_case      , __snake_case      , __snake_case   )      , __snake_case   ):
                        snake_case					=    _config_zero_init(getattr(__snake_case      , __snake_case   )   )
                        setattr(__snake_case      , __snake_case      , __snake_case   )
              return configs_no_init

         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         snake_case					=    _config_zero_init(__snake_case   )
         for model_class in self.all_model_classes:
              snake_case					=    model_class(config=__snake_case   )
              for name, param in model.named_parameters():
                   if param.requires_grad:
                        self.assertIn(
                            ((param.data.mean() * 1E9) / 1E9).round().item()      , [0.0, 1.0]      , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      , )




    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
    def 		a_      (	self   ):
         pass






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
     return image

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







    """simple docstring"""

    @cached_property
    def 		a_      (	self   ):
         return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''   ) if is_vision_available() else None




    @slow
    def 		a_      (	self   ):
         snake_case					=    SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''   ).to(__snake_case   )

         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(images=__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )

         # forward pass
         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         # verify the logits
         snake_case					=    torch.Size((1, 1_0_0_0)   )
         self.assertEqual(outputs.logits.shape      , __snake_case   )

         snake_case					=    torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]   ).to(__snake_case   )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]      , __snake_case      , atol=1E-4   )   )








import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=1_3      , __snake_case=7      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=True      , __snake_case=9_9      , __snake_case=3_2      , __snake_case=5      , __snake_case=4      , __snake_case=4      , __snake_case="gelu"      , __snake_case=0.0      , __snake_case=0.1      , __snake_case=True      , __snake_case=5_1_2      , __snake_case=1_6      , __snake_case=2      , __snake_case=0.02      , __snake_case=3      , __snake_case=4      , __snake_case=None      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    seq_length
         snake_case					=    is_training
         snake_case					=    use_input_mask
         snake_case					=    use_token_type_ids
         snake_case					=    use_labels
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    intermediate_multiple_size
         snake_case					=    hidden_act
         snake_case					=    hidden_dropout
         snake_case					=    attention_dropout
         snake_case					=    weight_tying
         snake_case					=    max_position_embeddings
         snake_case					=    type_vocab_size
         snake_case					=    type_sequence_label_size
         snake_case					=    initializer_range
         snake_case					=    num_labels
         snake_case					=    num_choices
         snake_case					=    scope
    def 		a_      (	self   ):
         snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size   )

         snake_case					=    None
         if self.use_input_mask:
              snake_case					=    random_attention_mask([self.batch_size, self.seq_length]   )

         snake_case					=    None
         if self.use_labels:
              snake_case					=    ids_tensor([self.batch_size, self.seq_length]      , self.num_labels   )

         snake_case					=    self.get_config()

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self   ):
         return GPTNeoXJapaneseConfig(
             vocab_size=self.vocab_size      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_multiple_size=self.intermediate_multiple_size      , hidden_act=self.hidden_act      , hidden_dropout=self.hidden_dropout      , attention_dropout=self.attention_dropout      , weight_tying=self.weight_tying      , max_position_embeddings=self.max_position_embeddings      , type_vocab_size=self.type_vocab_size      , is_decoder=__snake_case      , initializer_range=self.initializer_range      , )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()

         snake_case					=    True

         return config, input_ids, input_mask, token_labels
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseModel(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         snake_case					=    model(__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseModel(__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case   )
         self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , labels=__snake_case   )
         self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    True
         snake_case					=    GPTNeoXJapaneseForCausalLM(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         # first forward pass
         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , use_cache=__snake_case   )
         snake_case					=    outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         snake_case					=    ids_tensor((self.batch_size, 3)      , config.vocab_size   )
         snake_case					=    ids_tensor((self.batch_size, 3)      , vocab_size=2   )

         # append to next input_ids and
         snake_case					=    torch.cat([input_ids, next_tokens]      , dim=-1   )
         snake_case					=    torch.cat([input_mask, next_mask]      , dim=-1   )

         snake_case					=    model(__snake_case      , attention_mask=__snake_case      , output_hidden_states=__snake_case   )
         snake_case					=    output_from_no_past['''hidden_states'''][0]
         snake_case					=    model(
             __snake_case      , attention_mask=__snake_case      , past_key_values=__snake_case      , output_hidden_states=__snake_case      , )['''hidden_states'''][0]

         # select random slice
         snake_case					=    ids_tensor((1,)      , output_from_past.shape[-1]   ).item()
         snake_case					=    output_from_no_past[:, -3:, random_slice_idx].detach()
         snake_case					=    output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(__snake_case      , __snake_case      , atol=1E-3   )   )




    def 		a_      (	self   ):
         snake_case					=    self.prepare_config_and_inputs()
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    config_and_inputs
         snake_case					=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
    __magic_name__        =		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
    __magic_name__        =		(
        {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
        if is_torch_available()
        else {}
    )
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    GPTNeoXJapaneseModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , hidden_size=3_7   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         # This regression test was failing with PyTorch < 1.3
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_decoder()

         snake_case					=    None

         self.model_tester.create_and_check_model_as_decoder(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*__snake_case   )




    @slow
    def 		a_      (	self   ):
         snake_case					=    '''abeja/gpt-neox-japanese-2.7b'''

         snake_case					=    ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

         snake_case					=    [
             '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
             '''100年後に必要とされる会社は、「人」が中心の会社です。''',
             '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
             '''国境の長いトンネルを抜けると、そこは雪国だった。''',
             '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
         ]

         snake_case					=    GPTNeoXJapaneseTokenizer.from_pretrained(__snake_case   )
         snake_case					=    GPTNeoXJapaneseForCausalLM.from_pretrained(__snake_case   )

         snake_case					=    []
         for prompt in prompts:
              snake_case					=    tokenizer(__snake_case      , return_tensors='''pt'''   ).input_ids
              snake_case					=    model.generate(__snake_case      , max_length=5_0   )
              snake_case					=    tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case   )
              predicted_outputs += generated_string
         self.assertListEqual(__snake_case      , __snake_case   )




