
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   :
					'''simple docstring'''






					A__      =			XGLMConfig
					A__      =			{}
					A__      =			'''gelu'''

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




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		seq_length
							lowercase__					      =		is_training
							lowercase__					      =		use_input_mask
							lowercase__					      =		use_labels
							lowercase__					      =		vocab_size
							lowercase__					      =		d_model
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		ffn_dim
							lowercase__					      =		activation_function
							lowercase__					      =		activation_dropout
							lowercase__					      =		attention_dropout
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		initializer_range
							lowercase__					      =		None
							lowercase__					      =		0
							lowercase__					      =		2
							lowercase__					      =		1

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




							"""simple docstring"""


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

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




							"""simple docstring"""


							lowercase__					      =		tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size		)				, clip_value_min=0				, clip_value_max=3		)

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

							lowercase__					      =		self.get_config()

							lowercase__					      =		floats_tensor([self.num_hidden_layers, self.num_attention_heads]				, 2		)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)

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




							"""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=_a				, bos_token_id=self.bos_token_id				, eos_token_id=self.eos_token_id				, pad_token_id=self.pad_token_id				, return_dict=_a				, )


					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()

							(
							    (
							    lowercase__				
							)      ,     (
							    lowercase__				
							)      ,     (
							    lowercase__				
							)      ,     (
							    lowercase__				
							)      ,     				
							)					      =		config_and_inputs

							lowercase__					      =		{
							    """input_ids""": input_ids,
							    """head_mask""": head_mask,
							}

							return config, inputs_dict






@require_tf
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					A__      =			(TFXGLMForCausalLM,) if is_tf_available() else ()
					A__      =			(
					    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
					)
					A__      =			False
					A__      =			False
					A__      =			False

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




							"""simple docstring"""


							lowercase__					      =		TFXGLMModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_a				, n_embd=37		)

					def   lowerCamelCase__					(self   :						Any		)						->       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]:
									lowercase__					      =		TFXGLMModel.from_pretrained(_a		)
									self.assertIsNotNone(_a		)


					@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""		)
					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							super().test_resize_token_embeddings()






@require_tf
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						str=True		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
							lowercase__					      =		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
							lowercase__					      =		[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
							lowercase__					      =		model.generate(_a				, do_sample=_a				, num_beams=1		)
							if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()				, _a		)

					@slow
					def   lowerCamelCase__					(self   :						Dict		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)
							lowercase__					      =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)

							tf.random.set_seed(0		)
							lowercase__					      =		tokenizer("""Today is a nice day and"""				, return_tensors="""tf"""		)
							lowercase__					      =		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"""		):
									lowercase__					      =		model.generate(_a				, do_sample=_a				, seed=[7, 0]		)
							lowercase__					      =		tokenizer.decode(output_ids[0]				, skip_special_tokens=_a		)

							lowercase__					      =		(
							    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
							)
							self.assertEqual(_a				, _a		)


					@slow
					def   lowerCamelCase__					(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							lowercase__					      =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""		)
							lowercase__					      =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""		)

							lowercase__					      =		"""left"""

							# use different length sentences to test batching
							lowercase__					      =		[
							    """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""",
							]

							lowercase__					      =		tokenizer(_a				, return_tensors="""tf"""				, padding=_a		)
							lowercase__					      =		inputs["""input_ids"""]

							lowercase__					      =		model.generate(input_ids=_a				, attention_mask=inputs["""attention_mask"""]				, max_new_tokens=12		)

							lowercase__					      =		tokenizer(sentences[0]				, return_tensors="""tf"""		).input_ids
							lowercase__					      =		model.generate(input_ids=_a				, max_new_tokens=12		)

							lowercase__					      =		tokenizer(sentences[1]				, return_tensors="""tf"""		).input_ids
							lowercase__					      =		model.generate(input_ids=_a				, max_new_tokens=12		)

							lowercase__					      =		tokenizer.batch_decode(_a				, skip_special_tokens=_a		)
							lowercase__					      =		tokenizer.decode(output_non_padded[0]				, skip_special_tokens=_a		)
							lowercase__					      =		tokenizer.decode(output_padded[0]				, skip_special_tokens=_a		)

							lowercase__					      =		[
							    """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(_a				, _a		)
							self.assertListEqual(_a				, [non_padded_sentence, padded_sentence]		)





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
	from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image



class 							A   :
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Any=13				, _UpperCAmelCase   :						int=64				, _UpperCAmelCase   :						Any=2				, _UpperCAmelCase   :						str=3				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						List[Any]=True				, _UpperCAmelCase   :						Optional[int]=32				, _UpperCAmelCase   :						Tuple=5				, _UpperCAmelCase   :						Tuple=4				, _UpperCAmelCase   :						Union[str, Any]=37				, _UpperCAmelCase   :						Tuple="gelu"				, _UpperCAmelCase   :						Optional[int]=0.1				, _UpperCAmelCase   :						int=0.1				, _UpperCAmelCase   :						Any=10				, _UpperCAmelCase   :						str=0.02				, _UpperCAmelCase   :						Any=[1, 16, 4, 4]				, _UpperCAmelCase   :						str=None				, )						->       int:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		patch_size
							lowercase__					      =		num_channels
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		type_sequence_label_size
							lowercase__					      =		initializer_range
							lowercase__					      =		scope
							lowercase__					      =		backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							lowercase__					      =		(self.image_size // 32) ** 2
							lowercase__					      =		num_patches + 1

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




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowercase__					      =		None
							if self.use_labels:
									lowercase__					      =		ids_tensor([self.batch_size]				, self.type_sequence_label_size		)

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		{
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [4, 8, 16, 32],
							    """num_groups""": 2,
							}

							return ViTHybridConfig(
							    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=_UpperCAmelCase				, initializer_range=self.initializer_range				, backbone_featmap_shape=self.backbone_featmap_shape				, backbone_config=_UpperCAmelCase				, )

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						int		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		self.type_sequence_label_size
							lowercase__					      =		ViTHybridForImageClassification(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase				, labels=_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)		)


					def   lowerCamelCase__					(self   :						int		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					A__      =			(
					    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ViTHybridModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						Dict		)						->       List[str]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					@unittest.skip(reason="""ViT does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						List[str]		)						->       Any:




							"""simple docstring"""


							pass

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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Tuple		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Dict		)						->       Any:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					      =		_config_zero_init(_UpperCAmelCase		)
							for model_class in self.all_model_classes:
									lowercase__					      =		model_class(config=_UpperCAmelCase		)
									# Skip the check for the backbone
									for name, module in model.named_modules():
											if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
													lowercase__					      =		[f'''{name}.{key}''' for key in module.state_dict().keys()]
													break

									for name, param in model.named_parameters():
											if param.requires_grad:
													if name in backbone_params:
															continue
													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'''				, )


					@slow
					def   lowerCamelCase__					(self   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		ViTHybridModel.from_pretrained(_UpperCAmelCase		)
									self.assertIsNotNone(_UpperCAmelCase		)






def 			UpperCamelCase (     )       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image







@require_torch
@require_vision
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@cached_property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Union[str, Any]:




							"""simple docstring"""


							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
							    if is_vision_available()
							    else None
							)

					@slow
					def   lowerCamelCase__					(self   :						List[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
							    _UpperCAmelCase		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase		)

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

							lowercase__					      =		torch.tensor([-1.9_090, -0.4_993, -0.2_389]		).to(_UpperCAmelCase		)

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


					@slow
					@require_accelerate
					def   lowerCamelCase__					(self   :						Any		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""		)
							lowercase__					      =		ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""				, device_map="""auto"""		)

							lowercase__					      =		prepare_img()

							lowercase__					      =		image_processor(images=_UpperCAmelCase				, return_tensors="""pt"""		)
							lowercase__					      =		model(**_UpperCAmelCase		)
							lowercase__					      =		outputs.logits
							# model predicts one of the 1000 ImageNet classes
							lowercase__					      =		logits.argmax(-1		).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]				, """tabby, tabby cat"""		)

