"""simple docstring"""


import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=13,							lowerCAmelCase__=30,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=32,							lowerCAmelCase__=5,							lowerCAmelCase__=4,							lowerCAmelCase__=37,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=10,							lowerCAmelCase__=0.02,							lowerCAmelCase__=None,							lowerCAmelCase__=2,							)      ->      Any:
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					image_size
            snake_case_					       =					patch_size
            snake_case_					       =					num_channels
            snake_case_					       =					is_training
            snake_case_					       =					use_labels
            snake_case_					       =					hidden_size
            snake_case_					       =					num_hidden_layers
            snake_case_					       =					num_attention_heads
            snake_case_					       =					intermediate_size
            snake_case_					       =					hidden_act
            snake_case_					       =					hidden_dropout_prob
            snake_case_					       =					attention_probs_dropout_prob
            snake_case_					       =					type_sequence_label_size
            snake_case_					       =					initializer_range
            snake_case_					       =					scope
            snake_case_					       =					encoder_stride

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







       def 		a_						( self)      ->      Any:
            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.type_sequence_label_size)

            snake_case_					       =					self.get_config()

            return config, pixel_values, labels







       def 		a_						( self)      ->      Union[str, Any]:
            return ViTConfig(
                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=lowerCAmelCase__,							initializer_range=self.initializer_range,							encoder_stride=self.encoder_stride,							)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Union[str, Any]:
            snake_case_					       =					ViTModel(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.last_hidden_state.shape,							(self.batch_size, self.seq_length, self.hidden_size))







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      str:
            snake_case_					       =					ViTForMaskedImageModeling(config=lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(
                result.reconstruction.shape,							(self.batch_size, self.num_channels, self.image_size, self.image_size))

            # test greyscale images
            snake_case_					       =					1
            snake_case_					       =					ViTForMaskedImageModeling(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()

            snake_case_					       =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.reconstruction.shape,							(self.batch_size, 1, self.image_size, self.image_size))







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					self.type_sequence_label_size
            snake_case_					       =					ViTForImageClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()
            snake_case_					       =					model(lowerCAmelCase__,							labels=lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size))

            # test greyscale images
            snake_case_					       =					1
            snake_case_					       =					ViTForImageClassification(lowerCAmelCase__)
            model.to(lowerCAmelCase__)
            model.eval()

            snake_case_					       =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
            snake_case_					       =					model(lowerCAmelCase__)
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size))







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.prepare_config_and_inputs()
            (
                (
                snake_case_
            )       ,	(
                snake_case_
            )       ,	(
                snake_case_
            )       ,	
            )					       =					config_and_inputs
            snake_case_					       =					{'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      (
           (
               ViTModel,
               ViTForImageClassification,
               ViTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_			=      (
           {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_			=      True

       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					ViTModelTester(self)
            snake_case_					       =					ConfigTester(self,							config_class=lowerCAmelCase__,							has_text_modality=lowerCAmelCase__,							hidden_size=37)







       def 		a_						( self)      ->      str:
            self.config_tester.run_common_tests()







       @unittest.skip(reason='ViT does not use inputs_embeds')
       def 		a_						( self)      ->      List[str]:
            pass







       def 		a_						( self)      ->      Optional[Any]:
            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(lowerCAmelCase__)
                 self.assertIsInstance(model.get_input_embeddings(),							(nn.Module))
                 snake_case_					       =					model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCAmelCase__,							nn.Linear))







       def 		a_						( self)      ->      List[str]:
            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(lowerCAmelCase__)
                 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],							lowerCAmelCase__)







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__)







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)







       @slow
       def 		a_						( self)      ->      str:
            for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case_					       =					ViTModel.from_pretrained(lowerCAmelCase__)
                 self.assertIsNotNone(lowerCAmelCase__)







def 					UpperCAmelCase    (		)				->      List[Any]:
     snake_case_					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
     return image




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







       @cached_property
       def 		a_						( self)      ->      Dict:
            return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') if is_vision_available() else None







       @slow
       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(lowerCAmelCase__)

            snake_case_					       =					self.default_image_processor
            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt').to(lowerCAmelCase__)

            # forward pass
            with torch.no_grad():
                 snake_case_					       =					model(**lowerCAmelCase__)

            # verify the logits
            snake_case_					       =					torch.Size((1, 1000))
            self.assertEqual(outputs.logits.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor([-0.2744, 0.8215, -0.0836]).to(lowerCAmelCase__)

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







       @slow
       def 		a_						( self)      ->      List[Any]:
            # ViT models have an `interpolate_pos_encoding` argument in their forward method,
            # allowing to interpolate the pre-trained position embeddings in order to use
            # the model on higher resolutions. The DINO model by Facebook AI leverages this
            # to visualize self-attention on higher resolution images.
            snake_case_					       =					ViTModel.from_pretrained('facebook/dino-vits8').to(lowerCAmelCase__)

            snake_case_					       =					ViTImageProcessor.from_pretrained('facebook/dino-vits8',							size=480)
            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt')
            snake_case_					       =					inputs.pixel_values.to(lowerCAmelCase__)

            # forward pass
            with torch.no_grad():
                 snake_case_					       =					model(lowerCAmelCase__,							interpolate_pos_encoding=lowerCAmelCase__)

            # verify the logits
            snake_case_					       =					torch.Size((1, 3601, 384))
            self.assertEqual(outputs.last_hidden_state.shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor(
                [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]).to(lowerCAmelCase__)

            self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3],							lowerCAmelCase__,							atol=1e-4))







       @slow
       @require_accelerate
       @require_torch_gpu
       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					ViTModel.from_pretrained('facebook/dino-vits8',							torch_dtype=torch.floataa,							device_map='auto')
            snake_case_					       =					self.default_image_processor

            snake_case_					       =					prepare_img()
            snake_case_					       =					image_processor(images=lowerCAmelCase__,							return_tensors='pt')
            snake_case_					       =					inputs.pixel_values.to(lowerCAmelCase__)

            # forward pass to make sure inference works in fp16
            with torch.no_grad():
                 snake_case_					       =					model(lowerCAmelCase__)





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )




@require_tf
class A  :
      UpperCamelCase__							:  Union[str, Any]      =XGLMConfig
      UpperCamelCase__							:  Dict      ={}
      UpperCamelCase__							:  Tuple      ='gelu'






      def __init__(					self						:     List[Any]			,   lowercase_						:     List[str]			,   lowercase_						:     Union[str, Any]=14			,   lowercase_						:     Dict=7			,   lowercase_						:     Union[str, Any]=True			,   lowercase_						:     Optional[Any]=True			,   lowercase_						:     Any=True			,   lowercase_						:     Optional[int]=99			,   lowercase_						:     List[Any]=32			,   lowercase_						:     List[Any]=2			,   lowercase_						:     Dict=4			,   lowercase_						:     List[str]=37			,   lowercase_						:     int="gelu"			,   lowercase_						:     List[Any]=0.1			,   lowercase_						:     Union[str, Any]=0.1			,   lowercase_						:     List[str]=512			,   lowercase_						:     Union[str, Any]=0.02			,   )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=parent
             _lowerCamelCase       :		Optional[Any]		=batch_size
             _lowerCamelCase       :		Optional[int]		=seq_length
             _lowerCamelCase       :		Union[str, Any]		=is_training
             _lowerCamelCase       :		Tuple		=use_input_mask
             _lowerCamelCase       :		str		=use_labels
             _lowerCamelCase       :		Any		=vocab_size
             _lowerCamelCase       :		List[str]		=d_model
             _lowerCamelCase       :		List[Any]		=num_hidden_layers
             _lowerCamelCase       :		Union[str, Any]		=num_attention_heads
             _lowerCamelCase       :		List[Any]		=ffn_dim
             _lowerCamelCase       :		Optional[Any]		=activation_function
             _lowerCamelCase       :		Dict		=activation_dropout
             _lowerCamelCase       :		Tuple		=attention_dropout
             _lowerCamelCase       :		List[str]		=max_position_embeddings
             _lowerCamelCase       :		int		=initializer_range
             _lowerCamelCase       :		Optional[int]		=None
             _lowerCamelCase       :		Optional[Any]		=0
             _lowerCamelCase       :		List[str]		=2
             _lowerCamelCase       :		Any		=1






      def        lowerCamelCase       (					self						:     str )					->						int:
             """simple docstring"""




             return XGLMConfig.from_pretrained('facebook/xglm-564M' )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=tf.clip_by_value(
                 ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size )			,   clip_value_min=0			,   clip_value_max=3 )

             _lowerCamelCase       :		Any		=None
             if self.use_input_mask:
                    _lowerCamelCase       :		str		=random_attention_mask([self.batch_size, self.seq_length] )

             _lowerCamelCase       :		Optional[int]		=self.get_config()

             _lowerCamelCase       :		Optional[Any]		=floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,   2 )

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
             )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             return XGLMConfig(
                 vocab_size=self.vocab_size			,   d_model=self.hidden_size			,   num_layers=self.num_hidden_layers			,   attention_heads=self.num_attention_heads			,   ffn_dim=self.ffn_dim			,   activation_function=self.activation_function			,   activation_dropout=self.activation_dropout			,   attention_dropout=self.attention_dropout			,   max_position_embeddings=self.max_position_embeddings			,   initializer_range=self.initializer_range			,   use_cache=lowercase_			,   bos_token_id=self.bos_token_id			,   eos_token_id=self.eos_token_id			,   pad_token_id=self.pad_token_id			,   return_dict=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     
             )       :		Any		=config_and_inputs

             _lowerCamelCase       :		Union[str, Any]		={
                 'input_ids': input_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict




@require_tf
class A  (   UpperCamelCase_    ,							UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Union[str, Any]      =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
      UpperCamelCase__							:  List[str]      =(TFXGLMForCausalLM,) if is_tf_available() else ()
      UpperCamelCase__							:  Any      =(
          {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
      )
      UpperCamelCase__							:  str      =False
      UpperCamelCase__							:  int      =False
      UpperCamelCase__							:  int      =False






      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=TFXGLMModelTester(self )
             _lowerCamelCase       :		str		=ConfigTester(self			,   config_class=lowercase_			,   n_embd=37 )






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.config_tester.run_common_tests()






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						int:
             """simple docstring"""




             for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase       :		int		=TFXGLMModel.from_pretrained(lowercase_ )
                    self.assertIsNotNone(lowercase_ )






      @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             super().test_resize_token_embeddings()




@require_tf
class A  (   unittest.TestCase     ):






      @slow
      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=True )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		List[Any]		=tf.convert_to_tensor([[2, 268, 9865]]			,   dtype=tf.intaa )  # The dog
             # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
             # fmt: off
             _lowerCamelCase       :		int		=[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
             # fmt: on
             _lowerCamelCase       :		Dict		=model.generate(lowercase_			,   do_sample=lowercase_			,   num_beams=1 )
             if verify_outputs:
                    self.assertListEqual(output_ids[0].numpy().tolist()			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

             tf.random.set_seed(0 )
             _lowerCamelCase       :		Tuple		=tokenizer('Today is a nice day and'			,   return_tensors='tf' )
             _lowerCamelCase       :		Optional[int]		=tokenized.input_ids
             # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
             with tf.device(':/CPU:0' ):
                    _lowerCamelCase       :		List[Any]		=model.generate(lowercase_			,   do_sample=lowercase_			,   seed=[7, 0] )
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.decode(output_ids[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		Union[str, Any]		=(
                 'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
             )
             self.assertEqual(lowercase_			,   lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
             _lowerCamelCase       :		Any		=XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

             _lowerCamelCase       :		Optional[Any]		='left'

             # use different length sentences to test batching
             _lowerCamelCase       :		int		=[
                 'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                 'left-padding, such as in batched generation. The output for the sequence below should be the same '
                 'regardless of whether left padding is applied or not. When',
                 'Hello, my dog is a little',
             ]

             _lowerCamelCase       :		List[Any]		=tokenizer(lowercase_			,   return_tensors='tf'			,   padding=lowercase_ )
             _lowerCamelCase       :		int		=inputs['input_ids']

             _lowerCamelCase       :		str		=model.generate(input_ids=lowercase_			,   attention_mask=inputs['attention_mask']			,   max_new_tokens=12 )

             _lowerCamelCase       :		Optional[Any]		=tokenizer(sentences[0]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		List[str]		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		Tuple		=tokenizer(sentences[1]			,   return_tensors='tf' ).input_ids
             _lowerCamelCase       :		Dict		=model.generate(input_ids=lowercase_			,   max_new_tokens=12 )

             _lowerCamelCase       :		str		=tokenizer.batch_decode(lowercase_			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=tokenizer.decode(output_non_padded[0]			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		int		=tokenizer.decode(output_padded[0]			,   skip_special_tokens=lowercase_ )

             _lowerCamelCase       :		List[str]		=[
                 'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
                 'left-padding, such as in batched generation. The output for the sequence below should be the same '
                 'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
                 'a single',
                 'Hello, my dog is a little bit of a shy one, but he is very friendly',
             ]
             self.assertListEqual(lowercase_			,   lowercase_ )
             self.assertListEqual(lowercase_			,   [non_padded_sentence, padded_sentence] )


