'''simple docstring'''




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ImageGPTImageProcessor





class 					__magic_name__		(					unittest.TestCase):







			def __init__(			self       :						Any       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						int=7       ,    lowercase_       :						Any=3       ,    lowercase_       :						Tuple=18       ,    lowercase_       :						List[Any]=30       ,    lowercase_       :						Union[str, Any]=400       ,    lowercase_       :						Tuple=True       ,    lowercase_       :						int=None       ,    lowercase_       :						List[Any]=True       ,    ):
										lowercase_      :    List[str]       		= size if size is not None else {"""height""": 18, """width""": 18}
										lowercase_      :    Any       		= parent
										lowercase_      :    Union[str, Any]       		= batch_size
										lowercase_      :    Tuple       		= num_channels
										lowercase_      :    int       		= image_size
										lowercase_      :    Optional[Any]       		= min_resolution
										lowercase_      :    int       		= max_resolution
										lowercase_      :    List[Any]       		= do_resize
										lowercase_      :    Tuple       		= size
										lowercase_      :    Union[str, Any]       		= do_normalize







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return {
										    # here we create 2 clusters for the sake of simplicity
										    "clusters": np.asarray(
										        [
										            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
										            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
										        ]					),
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										}





@require_torch
@require_vision
class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = ImageGPTImageProcessor if is_vision_available() else None







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    List[str]       		= ImageGPTImageProcessingTester(self					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Dict       		= self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowercase_       ,    """clusters"""					)					)
										self.assertTrue(hasattr(lowercase_       ,    """do_resize"""					)					)
										self.assertTrue(hasattr(lowercase_       ,    """size"""					)					)
										self.assertTrue(hasattr(lowercase_       ,    """do_normalize"""					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    int       		= self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18}					)

										lowercase_      :    str       		= self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42					)
										self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42}					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    Optional[Any]       		= self.image_processing_class(**self.image_processor_dict					)
										lowercase_      :    str       		= json.loads(image_processor.to_json_string()					)
										for key, value in self.image_processor_dict.items():
																	if key == "clusters":
																								self.assertTrue(np.array_equal(lowercase_       ,    obj[key]					)					)
																	else:
																								self.assertEqual(obj[key]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Optional[Any]       		= self.image_processing_class(**self.image_processor_dict					)

										with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_      :    Optional[Any]       		= os.path.join(lowercase_       ,    """image_processor.json"""					)
																	image_processor_first.to_json_file(lowercase_					)
																	lowercase_      :    Optional[int]       		= self.image_processing_class.from_json_file(lowercase_					).to_dict()

										lowercase_      :    List[str]       		= image_processor_first.to_dict()
										for key, value in image_processor_first.items():
																	if key == "clusters":
																								self.assertTrue(np.array_equal(lowercase_       ,    image_processor_second[key]					)					)
																	else:
																								self.assertEqual(image_processor_first[key]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    List[Any]       		= self.image_processing_class(**self.image_processor_dict					)

										with tempfile.TemporaryDirectory() as tmpdirname:
																	image_processor_first.save_pretrained(lowercase_					)
																	lowercase_      :    Union[str, Any]       		= self.image_processing_class.from_pretrained(lowercase_					).to_dict()

										lowercase_      :    Any       		= image_processor_first.to_dict()
										for key, value in image_processor_first.items():
																	if key == "clusters":
																								self.assertTrue(np.array_equal(lowercase_       ,    image_processor_second[key]					)					)
																	else:
																								self.assertEqual(image_processor_first[key]       ,    lowercase_					)







			@unittest.skip("""ImageGPT requires clusters at initialization"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										pass




def 				lowerCamelCase			(   )    ->   int:
							lowercase_      :    int       		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,   split="""test"""				)

							lowercase_      :    Union[str, Any]       		= Image.open(dataset[4]["""file"""]				)
							lowercase_      :    Optional[Any]       		= Image.open(dataset[5]["""file"""]				)

							lowercase_      :    Optional[Any]       		= [imagea, imagea]

							return images





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







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Union[str, Any]       		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

										lowercase_      :    Tuple       		= prepare_images()

										# test non-batched
										lowercase_      :    Optional[Any]       		= image_processing(images[0]       ,    return_tensors="""pt"""					)

										self.assertIsInstance(encoding.input_ids       ,    torch.LongTensor					)
										self.assertEqual(encoding.input_ids.shape       ,    (1, 1024)					)

										lowercase_      :    Union[str, Any]       		= [306, 191, 191]
										self.assertEqual(encoding.input_ids[0, :3].tolist()       ,    lowercase_					)

										# test batched
										lowercase_      :    int       		= image_processing(lowercase_       ,    return_tensors="""pt"""					)

										self.assertIsInstance(encoding.input_ids       ,    torch.LongTensor					)
										self.assertEqual(encoding.input_ids.shape       ,    (2, 1024)					)

										lowercase_      :    Dict       		= [303, 13, 13]
										self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,    lowercase_					)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:				Optional[Any]	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, Any]	       =		{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''speech_to_text'''
			UpperCamelCase__       = ['''past_key_values''']
			UpperCamelCase__       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(			self       :						str       ,    lowercase_       :						Optional[int]=10000       ,    lowercase_       :						int=12       ,    lowercase_       :						Any=2048       ,    lowercase_       :						Any=4       ,    lowercase_       :						Dict=6       ,    lowercase_       :						Any=2048       ,    lowercase_       :						List[str]=4       ,    lowercase_       :						str=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						int="relu"       ,    lowercase_       :						str=256       ,    lowercase_       :						int=0.1       ,    lowercase_       :						int=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Optional[int]=0.02       ,    lowercase_       :						str=2       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						Any=1       ,    lowercase_       :						Dict=0       ,    lowercase_       :						List[str]=2       ,    lowercase_       :						List[Any]=6000       ,    lowercase_       :						Tuple=1024       ,    lowercase_       :						str=2       ,    lowercase_       :						Any=(5, 5)       ,    lowercase_       :						Union[str, Any]=1024       ,    lowercase_       :						Dict=80       ,    lowercase_       :						List[Any]=1       ,    **lowercase_       :						int       ,    ):
										lowercase_      :    List[Any]       		= vocab_size
										lowercase_      :    str       		= d_model
										lowercase_      :    List[Any]       		= encoder_ffn_dim
										lowercase_      :    str       		= encoder_layers
										lowercase_      :    Dict       		= encoder_attention_heads
										lowercase_      :    str       		= decoder_ffn_dim
										lowercase_      :    int       		= decoder_layers
										lowercase_      :    Any       		= decoder_attention_heads
										lowercase_      :    Any       		= dropout
										lowercase_      :    Dict       		= attention_dropout
										lowercase_      :    Optional[int]       		= activation_dropout
										lowercase_      :    Any       		= activation_function
										lowercase_      :    Union[str, Any]       		= init_std
										lowercase_      :    str       		= encoder_layerdrop
										lowercase_      :    Optional[int]       		= decoder_layerdrop
										lowercase_      :    Dict       		= use_cache
										lowercase_      :    Union[str, Any]       		= encoder_layers
										lowercase_      :    Tuple       		= scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase_      :    Dict       		= max_source_positions
										lowercase_      :    Optional[int]       		= max_target_positions
										lowercase_      :    Tuple       		= num_conv_layers
										lowercase_      :    Tuple       		= list(lowercase_					)
										lowercase_      :    Union[str, Any]       		= conv_channels
										lowercase_      :    str       		= input_feat_per_channel
										lowercase_      :    str       		= input_channels

										if len(self.conv_kernel_sizes					) != self.num_conv_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
																	    f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '''
																	    f'''`config.num_conv_layers = {self.num_conv_layers}`.'''					)

										super().__init__(
										    pad_token_id=lowercase_       ,    bos_token_id=lowercase_       ,    eos_token_id=lowercase_       ,    is_encoder_decoder=lowercase_       ,    decoder_start_token_id=lowercase_       ,    **lowercase_       ,    )





