




'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConditionalDetrImageProcessor





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,	__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=1 / 255     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	do_resize
            __snake_case            =	size
            __snake_case            =	do_normalize
            __snake_case            =	image_mean
            __snake_case            =	image_std
            __snake_case            =	do_rescale
            __snake_case            =	rescale_factor
            __snake_case            =	do_pad


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False	)   ->	Any:



            '''simple docstring'''




            if not batched:
                 __snake_case            =	image_inputs[0]
                 if isinstance(__SCREAMING_SNAKE_CASE     ,	Image.Image	):
                      __snake_case            =	image.size
                 else:
                      __snake_case            =	image.shape[1], image.shape[2]
                 if w < h:
                      __snake_case            =	int(self.size['''shortest_edge'''] * h / w	)
                      __snake_case            =	self.size["""shortest_edge"""]
                 elif w > h:
                      __snake_case            =	self.size["""shortest_edge"""]
                      __snake_case            =	int(self.size['''shortest_edge'''] * w / h	)
                 else:
                      __snake_case            =	self.size["""shortest_edge"""]
                      __snake_case            =	self.size["""shortest_edge"""]

            else:
                 __snake_case            =	[]
                 for image in image_inputs:
                      __snake_case            =	self.get_expected_values([image]	)
                      expected_values.append((expected_height, expected_width)	)
                 __snake_case            =	max(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE	: item[0]	)[0]
                 __snake_case            =	max(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE	: item[1]	)[1]

            return expected_height, expected_width





@require_torch
@require_vision
class      lowerCAmelCase (			A_					, unittest.TestCase):
       __lowercase	:   Any										=      ConditionalDetrImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	ConditionalDetrImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''image_mean'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''image_std'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_normalize'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_resize'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''size'''	)	)


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 18, '''longest_edge''': 1333}	)
            self.assertEqual(image_processor.do_pad     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.image_processing_class.from_dict(
                self.image_processor_dict     ,	size=42     ,	max_size=84     ,	pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 42, '''longest_edge''': 84}	)
            self.assertEqual(image_processor.do_pad     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values

            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

            self.assertEqual(
                encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

            # Test batched
            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )     ,	)


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	numpify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values

            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

            self.assertEqual(
                encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values

            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE	)

            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )     ,	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	torchify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values

            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	)

            self.assertEqual(
                encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values

            __snake_case            =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE	)

            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )     ,	)


       @slow
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
            with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,	'''r'''	) as f:
                 __snake_case            =	json.loads(f.read()	)

            __snake_case            =	{"""image_id""": 3_9769, """annotations""": target}

            # encode them
            __snake_case            =	ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''	)
            __snake_case            =	image_processing(images=__SCREAMING_SNAKE_CASE     ,	annotations=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)

            # verify pixel values
            __snake_case            =	torch.Size([1, 3, 800, 1066]	)
            self.assertEqual(encoding['''pixel_values'''].shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)

            # verify area
            __snake_case            =	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify boxes
            __snake_case            =	torch.Size([6, 4]	)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-3	)	)
            # verify image_id
            __snake_case            =	torch.tensor([3_9769]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify is_crowd
            __snake_case            =	torch.tensor([0, 0, 0, 0, 0, 0]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify class_labels
            __snake_case            =	torch.tensor([75, 75, 63, 65, 17, 17]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify orig_size
            __snake_case            =	torch.tensor([480, 640]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify size
            __snake_case            =	torch.tensor([800, 1066]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,	__SCREAMING_SNAKE_CASE	)	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
            with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,	'''r'''	) as f:
                 __snake_case            =	json.loads(f.read()	)

            __snake_case            =	{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

            __snake_case            =	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''	)

            # encode them
            __snake_case            =	ConditionalDetrImageProcessor(format='''coco_panoptic'''	)
            __snake_case            =	image_processing(images=__SCREAMING_SNAKE_CASE     ,	annotations=__SCREAMING_SNAKE_CASE     ,	masks_path=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)

            # verify pixel values
            __snake_case            =	torch.Size([1, 3, 800, 1066]	)
            self.assertEqual(encoding['''pixel_values'''].shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)

            # verify area
            __snake_case            =	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify boxes
            __snake_case            =	torch.Size([6, 4]	)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-3	)	)
            # verify image_id
            __snake_case            =	torch.tensor([3_9769]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify is_crowd
            __snake_case            =	torch.tensor([0, 0, 0, 0, 0, 0]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify class_labels
            __snake_case            =	torch.tensor([17, 17, 63, 75, 75, 93]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify masks
            __snake_case            =	82_2873
            self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,	__SCREAMING_SNAKE_CASE	)
            # verify orig_size
            __snake_case            =	torch.tensor([480, 640]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,	__SCREAMING_SNAKE_CASE	)	)
            # verify size
            __snake_case            =	torch.tensor([800, 1066]	)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,	__SCREAMING_SNAKE_CASE	)	)










"""simple docstring"""
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 	lowercase__				:
   __UpperCAmelCase							       =					XGLMConfig
   __UpperCAmelCase							       =					{}
   __UpperCAmelCase							       =					'''gelu'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=14      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=0.02      ,   )  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		parent
      _lowerCamelCase	:					int   =		batch_size
      _lowerCamelCase	:					str   =		seq_length
      _lowerCamelCase	:					Any   =		is_training
      _lowerCamelCase	:					int   =		use_input_mask
      _lowerCamelCase	:					Union[str, Any]   =		use_labels
      _lowerCamelCase	:					str   =		vocab_size
      _lowerCamelCase	:					List[str]   =		d_model
      _lowerCamelCase	:					List[Any]   =		num_hidden_layers
      _lowerCamelCase	:					Dict   =		num_attention_heads
      _lowerCamelCase	:					int   =		ffn_dim
      _lowerCamelCase	:					str   =		activation_function
      _lowerCamelCase	:					Optional[int]   =		activation_dropout
      _lowerCamelCase	:					Tuple   =		attention_dropout
      _lowerCamelCase	:					Tuple   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		None
      _lowerCamelCase	:					Union[str, Any]   =		0
      _lowerCamelCase	:					List[Any]   =		2
      _lowerCamelCase	:					str   =		1





   def 		UpperCamelCase_  (							self)  ->  int:
      return XGLMConfig.from_pretrained("""facebook/xglm-564M""")





   def 		UpperCamelCase_  (							self)  ->  int:
      _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	:					str   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Union[str, Any]   =		random_attention_mask([self.batch_size, self.seq_length])

      _lowerCamelCase	:					Tuple   =		self.get_config()

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

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      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=SCREAMING_SNAKE_CASE      ,   bos_token_id=self.bos_token_id      ,   eos_token_id=self.eos_token_id      ,   pad_token_id=self.pad_token_id      ,   return_dict=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[Any]   =		self.prepare_config_and_inputs()

      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					str   =		config_and_inputs

      _lowerCamelCase	:					Optional[Any]   =		{
          """input_ids""": input_ids,
          """head_mask""": head_mask,
      }

      return config, inputs_dict







@require_tf
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __UpperCAmelCase							       =					(TFXGLMForCausalLM,) if is_tf_available() else ()
   __UpperCAmelCase							       =					(
       {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMModelTester(self)
      _lowerCamelCase	:					str   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   n_embd=37)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      self.config_tester.run_common_tests()





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Tuple   =		TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





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







@require_tf
class 	lowercase__				(					unittest.TestCase				):





   @slow
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=True)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Union[str, 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	:					Dict   =		[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	:					str   =		model.generate(SCREAMING_SNAKE_CASE      ,   do_sample=SCREAMING_SNAKE_CASE      ,   num_beams=1)
      if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()      ,   SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Tuple   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")

      tf.random.set_seed(0)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer("""Today is a nice day and"""      ,   return_tensors="""tf""")
      _lowerCamelCase	:					Any   =		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	:					Any   =		model.generate(SCREAMING_SNAKE_CASE      ,   do_sample=SCREAMING_SNAKE_CASE      ,   seed=[7, 0])
      _lowerCamelCase	:					List[str]   =		tokenizer.decode(output_ids[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

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





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Any   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")

      _lowerCamelCase	:					List[Any]   =		"""left"""

      # use different length sentences to test batching
      _lowerCamelCase	:					List[Any]   =		[
          """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	:					Union[str, Any]   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_tensors="""tf"""      ,   padding=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		inputs["""input_ids"""]

      _lowerCamelCase	:					List[Any]   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   attention_mask=inputs["""attention_mask"""]      ,   max_new_tokens=12)

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

      _lowerCamelCase	:					Tuple   =		tokenizer(sentences[1]      ,   return_tensors="""tf""").input_ids
      _lowerCamelCase	:					int   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   max_new_tokens=12)

      _lowerCamelCase	:					Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		tokenizer.decode(output_non_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer.decode(output_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		[
          """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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   [non_padded_sentence, padded_sentence])

