

import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						tempfile.mkdtemp()

						# fmt: off
						A__				:       Optional[int]	  =						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
						# fmt: on
						A__				:       Tuple	  =						os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["""vocab_file"""]     )
						with open(self.vocab_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

						A__				:       List[str]	  =						{
						    """do_resize""": True,
						    """size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.5, 0.5, 0.5],
						    """image_std""": [0.5, 0.5, 0.5],
						}
						A__				:       List[str]	  =						os.path.join(self.tmpdirname       ,			A__     )
						with open(self.image_processor_file       ,			"""w"""       ,			encoding="""utf-8"""     ) as fp:
							json.dump(A__       ,			A__     )






					def     __A     (					self       ,			**A__     ):
						return BertTokenizer.from_pretrained(self.tmpdirname       ,			**A__     )






					def     __A     (					self       ,			**A__     ):
						return ViTImageProcessor.from_pretrained(self.tmpdirname       ,			**A__     )






					def     __A     (					self     ):
						shutil.rmtree(self.tmpdirname     )






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						[np.random.randint(255       ,			size=(3, 30, 400)       ,			dtype=np.uinta     )]

						A__				:       List[str]	  =						[Image.fromarray(np.moveaxis(A__       ,			0       ,			-1     )     ) for x in image_inputs]

						return image_inputs






					def     __A     (					self     ):
						A__				:       List[Any]	  =						self.get_tokenizer()
						A__				:       Any	  =						self.get_image_processor()

						A__				:       Optional[int]	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						processor.save_pretrained(self.tmpdirname     )
						A__				:       List[Any]	  =						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

						self.assertEqual(processor.tokenizer.get_vocab()       ,			tokenizer.get_vocab()     )
						self.assertIsInstance(processor.tokenizer       ,			(BertTokenizer, BertTokenizerFast)     )

						self.assertEqual(processor.image_processor.to_json_string()       ,			image_processor.to_json_string()     )
						self.assertIsInstance(processor.image_processor       ,			A__     )






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()       ,			image_processor=self.get_image_processor()     )
						processor.save_pretrained(self.tmpdirname     )

						A__				:       Any	  =						self.get_tokenizer(bos_token="""(BOS)"""       ,			eos_token="""(EOS)"""     )
						A__				:       Any	  =						self.get_image_processor(do_normalize=A__       ,			padding_value=1.0     )

						A__				:       Optional[int]	  =						VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname       ,			bos_token="""(BOS)"""       ,			eos_token="""(EOS)"""       ,			do_normalize=A__       ,			padding_value=1.0     )

						self.assertEqual(processor.tokenizer.get_vocab()       ,			tokenizer_add_kwargs.get_vocab()     )
						self.assertIsInstance(processor.tokenizer       ,			(BertTokenizer, BertTokenizerFast)     )

						self.assertEqual(processor.image_processor.to_json_string()       ,			image_processor_add_kwargs.to_json_string()     )
						self.assertIsInstance(processor.image_processor       ,			A__     )






					def     __A     (					self     ):
						A__				:       Optional[int]	  =						self.get_image_processor()
						A__				:       Dict	  =						self.get_tokenizer()

						A__				:       Dict	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						A__				:       Any	  =						self.prepare_image_inputs()

						A__				:       Optional[Any]	  =						image_processor(A__       ,			return_tensors="""np"""     )
						A__				:       str	  =						processor(images=A__       ,			return_tensors="""np"""     )

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()       ,			input_processor[key].sum()       ,			delta=1e-2     )






					def     __A     (					self     ):
						A__				:       str	  =						self.get_image_processor()
						A__				:       List[str]	  =						self.get_tokenizer()

						A__				:       Any	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						A__				:       Optional[Any]	  =						"""lower newer"""

						A__				:       Optional[Any]	  =						processor(text=A__     )

						A__				:       str	  =						tokenizer(A__     )

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,			encoded_processor[key]     )






					def     __A     (					self     ):
						A__				:       int	  =						self.get_image_processor()
						A__				:       str	  =						self.get_tokenizer()

						A__				:       int	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						A__				:       List[Any]	  =						"""lower newer"""
						A__				:       Dict	  =						self.prepare_image_inputs()

						A__				:       Any	  =						processor(text=A__       ,			images=A__     )

						self.assertListEqual(list(inputs.keys()     )       ,			["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

						# test if it raises when no input is passed
						with self.assertRaises(A__     ):
							processor()






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						self.get_image_processor()
						A__				:       str	  =						self.get_tokenizer()

						A__				:       Dict	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						A__				:       Dict	  =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						A__				:       Dict	  =						processor.batch_decode(A__     )
						A__				:       List[Any]	  =						tokenizer.batch_decode(A__     )

						self.assertListEqual(A__       ,			A__     )






					def     __A     (					self     ):
						A__				:       str	  =						self.get_image_processor()
						A__				:       Optional[int]	  =						self.get_tokenizer()

						A__				:       Optional[int]	  =						VisionTextDualEncoderProcessor(tokenizer=A__       ,			image_processor=A__     )

						A__				:       Optional[Any]	  =						"""lower newer"""
						A__				:       Optional[Any]	  =						self.prepare_image_inputs()

						A__				:       str	  =						processor(text=A__       ,			images=A__     )

						self.assertListEqual(list(inputs.keys()     )       ,			processor.model_input_names     )






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Any    			=     {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						self.expand_attention_types_params(A__     )

						if len(self.attention_layers     ) != self.num_layers:
							raise ValueError(
							    """Configuration for convolutional module is incorrect. """
							    """It is required that `len(config.attention_layers)` == `config.num_layers` """
							    F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
							    F"""`config.num_layers = {self.num_layers}`. """
							    """`config.attention_layers` is prepared using `config.attention_types`. """
							    """Please verify the value of `config.attention_types` argument."""     )

						super().__init__(bos_token_id=A__       ,			eos_token_id=A__       ,			**A__     )






					@staticmethod
					def     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						super(A__       ,			self     ).generate_dummy_inputs(
						    A__       ,			batch_size=A__       ,			seq_length=A__       ,			is_pair=A__       ,			framework=A__     )

						# We need to order the input in the way they appears in the forward()
						A__				:       Optional[int]	  =						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
							else:
								import torch

								A__  ,  A__				:       Dict	  =						common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__				:       int	  =						seqlen + 2
								A__				:       int	  =						(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__				:       Dict	  =						[
								    (torch.zeros(A__     ), torch.zeros(A__     )) for _ in range(self.num_layers     )
								]

						A__				:       List[Any]	  =						common_inputs["""attention_mask"""]
						if self.use_past:
							A__				:       int	  =						ordered_inputs["""attention_mask"""].dtype
							A__				:       str	  =						torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__       ,			A__       ,			dtype=A__     )]       ,			dim=1     )

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



