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       lowerCamelCase_	:






    '''simple docstring'''






    a__      :		str         = XGLMConfig
    a__      :		Tuple         = {}
    a__      :		Union[str, Any]         = """gelu"""







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







    def      UpperCamelCase__  (      self)			->      Union[str, Any]:
        return XGLMConfig.from_pretrained('''facebook/xglm-564M''')







    def      UpperCamelCase__  (      self)			->      str:
        __UpperCamelCase	:str         =						tf.clip_by_value(
            ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size)					,					clip_value_min=0					,					clip_value_max=3)

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

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

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

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
        )







    def      UpperCamelCase__  (      self)			->      Optional[Any]:
        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      UpperCamelCase__  (      self)			->      str:
        __UpperCamelCase	:int         =						self.prepare_config_and_inputs()

        (
            (
            __UpperCamelCase	
        )    ,			(
            __UpperCamelCase	
        )    ,			(
            __UpperCamelCase	
        )    ,			(
            __UpperCamelCase	
        )    ,				
        )	:Union[str, Any]         =						config_and_inputs

        __UpperCamelCase	:Any         =						{
            '''input_ids''': input_ids,
            '''head_mask''': head_mask,
        }

        return config, inputs_dict


@require_tf
class       lowerCamelCase_	(  snake_case_		,      snake_case_		,      unittest.TestCase					):






    '''simple docstring'''






    a__      :		List[str]         = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    a__      :		Union[str, Any]         = (TFXGLMForCausalLM,) if is_tf_available() else ()
    a__      :		List[str]         = (
        {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
    )
    a__      :		List[Any]         = False
    a__      :		List[str]         = False
    a__      :		Any         = False







    def      UpperCamelCase__  (      self)			->      List[Any]:
        __UpperCamelCase	:int         =						TFXGLMModelTester(self)
        __UpperCamelCase	:Any         =						ConfigTester(self					,					config_class=A_					,					n_embd=37)







    def      UpperCamelCase__  (      self)			->      int:
        self.config_tester.run_common_tests()







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






        for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase	:Union[str, Any]         =						TFXGLMModel.from_pretrained(A_)
            self.assertIsNotNone(A_)







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


@require_tf
class       lowerCamelCase_	(  unittest.TestCase					):






    '''simple docstring'''






    @slow
    def      UpperCamelCase__  (      self					,					__lowercase=True)			->      List[Any]:
        __UpperCamelCase	:List[str]         =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
        __UpperCamelCase	:Dict         =						tf.convert_to_tensor([[2, 268, 9_865]]					,					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
        __UpperCamelCase	:Dict         =						[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
        # fmt: on
        __UpperCamelCase	:Any         =						model.generate(A_					,					do_sample=A_					,					num_beams=1)
        if verify_outputs:
            self.assertListEqual(output_ids[0].numpy().tolist()					,					A_)







    @slow
    def      UpperCamelCase__  (      self)			->      List[str]:
        __UpperCamelCase	:Dict         =						XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
        __UpperCamelCase	:Tuple         =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

        tf.random.set_seed(0)
        __UpperCamelCase	:Optional[Any]         =						tokenizer('''Today is a nice day and'''					,					return_tensors='''tf''')
        __UpperCamelCase	:List[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'''):
            __UpperCamelCase	:Optional[Any]         =						model.generate(A_					,					do_sample=A_					,					seed=[7, 0])
        __UpperCamelCase	:int         =						tokenizer.decode(output_ids[0]					,					skip_special_tokens=A_)

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







    @slow
    def      UpperCamelCase__  (      self)			->      Optional[Any]:
        __UpperCamelCase	:Optional[Any]         =						TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
        __UpperCamelCase	:Union[str, Any]         =						XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

        __UpperCamelCase	:Union[str, Any]         =						'''left'''

        # use different length sentences to test batching
        __UpperCamelCase	: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''',
            '''Hello, my dog is a little''',
        ]

        __UpperCamelCase	:Tuple         =						tokenizer(A_					,					return_tensors='''tf'''					,					padding=A_)
        __UpperCamelCase	:Dict         =						inputs['''input_ids''']

        __UpperCamelCase	:Union[str, Any]         =						model.generate(input_ids=A_					,					attention_mask=inputs['''attention_mask''']					,					max_new_tokens=12)

        __UpperCamelCase	:Any         =						tokenizer(sentences[0]					,					return_tensors='''tf''').input_ids
        __UpperCamelCase	:int         =						model.generate(input_ids=A_					,					max_new_tokens=12)

        __UpperCamelCase	:List[Any]         =						tokenizer(sentences[1]					,					return_tensors='''tf''').input_ids
        __UpperCamelCase	:Tuple         =						model.generate(input_ids=A_					,					max_new_tokens=12)

        __UpperCamelCase	:Optional[int]         =						tokenizer.batch_decode(A_					,					skip_special_tokens=A_)
        __UpperCamelCase	:Tuple         =						tokenizer.decode(output_non_padded[0]					,					skip_special_tokens=A_)
        __UpperCamelCase	:Tuple         =						tokenizer.decode(output_padded[0]					,					skip_special_tokens=A_)

        __UpperCamelCase	: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(A_					,					A_)
        self.assertListEqual(A_					,					[non_padded_sentence, padded_sentence])







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
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 DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




