





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    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''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			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




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






			def __init__(			self   :    int							,	__lowerCAmelCase   :    Union[str, Any]=5_02_57							,	__lowerCAmelCase   :    Tuple=20_48							,	__lowerCAmelCase   :    Tuple=20_48							,	__lowerCAmelCase   :    Union[str, Any]=24							,	__lowerCAmelCase   :    List[Any]=[[["global", "local"], 12]]							,	__lowerCAmelCase   :    Dict=16							,	__lowerCAmelCase   :    Optional[Any]=None							,	__lowerCAmelCase   :    Optional[Any]=2_56							,	__lowerCAmelCase   :    str="gelu_new"							,	__lowerCAmelCase   :    Optional[Any]=0.0							,	__lowerCAmelCase   :    str=0.0							,	__lowerCAmelCase   :    Dict=0.0							,	__lowerCAmelCase   :    List[str]=0.1							,	__lowerCAmelCase   :    int=1e-5							,	__lowerCAmelCase   :    List[Any]=0.0_2							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=5_02_56							,	__lowerCAmelCase   :    Dict=5_02_56							,	**__lowerCAmelCase   :    List[Any]							,	)	->	Dict:







					"""simple docstring"""






					A__        =   vocab_size
					A__        =   max_position_embeddings
					A__        =   hidden_size
					A__        =   num_layers
					A__        =   num_heads
					A__        =   intermediate_size
					A__        =   window_size
					A__        =   activation_function
					A__        =   resid_dropout
					A__        =   embed_dropout
					A__        =   attention_dropout
					A__        =   classifier_dropout
					A__        =   layer_norm_epsilon
					A__        =   initializer_range
					A__        =   use_cache

					A__        =   bos_token_id
					A__        =   eos_token_id

					A__        =   attention_types
					A__        =   self.expand_attention_types_params(__lowerCAmelCase   )

					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=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )







			@staticmethod
			def    a_       (			__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   []
					for item in attention_types:
							for _ in range(item[1]   ):
									attentions.extend(item[0]   )
					return attentions







def    __lowerCamelCase					(							__a   :Union[str, Any]    ,     __a   :List[str]    ,     __a   :List[str]    ,     __a   :List[Any]    )		->				Optional[Any]:






		"""simple docstring"""

		import torch

		A__        =   input.size()
		A__        =   len(__a    )
		A__        =   shape[dimension]

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

		A__        =   [slice(__a    )] * rank
		A__        =   indices
		A__        =   input[s]

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

		return sliced.permute(__a    )







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""simple docstring"""

		import torch

		A__        =   torch.arange(1    ,     __a    )
		A__        =   torch.remainder(__a    ,     __a    )
		A__        =   remainders == 0
		A__        =   candidates[divisor_indices]
		A__        =   torch.max(__a    )
		return largest_divisor, torch.div(__a    ,     __a    ,     rounding_mode="""floor"""    )






class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    Optional[int]   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					A__        =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}   )
					if self.use_past:
							self.fill_with_past_key_values_(__lowerCAmelCase							,	direction="""inputs"""   )
							A__        =   {0: """batch""", 1: """past_sequence + sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}

					return common_inputs






			@property
			def    a_       (			self   :    Union[str, Any]   )	->	int:







					"""simple docstring"""






					return self._config.num_heads






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    PreTrainedTokenizer							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional[TensorType] = None							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					A__        =   super(__lowerCAmelCase							,	self   ).generate_dummy_inputs(
					    __lowerCAmelCase							,	batch_size=__lowerCAmelCase							,	seq_length=__lowerCAmelCase							,	is_pair=__lowerCAmelCase							,	framework=__lowerCAmelCase   )

					# We need to order the input in the way they appears in the forward()
					A__        =   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__        =   common_inputs["""input_ids"""].shape
									# Not using the same length for past_key_values
									A__        =   seqlen + 2
									A__        =   (
									    batch,
									    self.num_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // self.num_attention_heads,
									)
									A__        =   [
									    (torch.zeros(__lowerCAmelCase   ), torch.zeros(__lowerCAmelCase   )) for _ in range(self.num_layers   )
									]

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

					return ordered_inputs







			@property
			def    a_       (			self   :    Dict   )	->	int:







					"""simple docstring"""






					return 13










import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor, CLIPProcessor


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




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







					"""simple docstring"""






					A__        =   tempfile.mkdtemp()

					# fmt: off
					A__        =   ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
					# fmt: on
					A__        =   dict(zip(__lowerCAmelCase							,	range(len(__lowerCAmelCase   )   )   )   )
					A__        =   ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__        =   {"""unk_token""": """<unk>"""}

					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""vocab_file"""]   )
					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""merges_file"""]   )
					with open(self.vocab_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write(json.dumps(__lowerCAmelCase   ) + """\n"""   )
					with open(self.merges_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write("""\n""".join(__lowerCAmelCase   )   )

					A__        =   {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__        =   os.path.join(self.tmpdirname							,	__lowerCAmelCase   )
					with open(self.image_processor_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							json.dump(__lowerCAmelCase							,	__lowerCAmelCase   )






			def    a_       (			self   :    Tuple							,	**__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]							,	**__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    List[str]							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname   )






			def    a_       (			self   :    str   )	->	Any:







					"""simple docstring"""






					A__        =   [np.random.randint(2_55							,	size=(3, 30, 4_00)							,	dtype=np.uinta   )]

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

					return image_inputs






			def    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.get_tokenizer()
					A__        =   self.get_rust_tokenizer()
					A__        =   self.get_image_processor()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_slow.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname							,	use_fast=__lowerCAmelCase   )

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_fast.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor_slow.tokenizer.get_vocab()							,	tokenizer_slow.get_vocab()   )
					self.assertEqual(processor_fast.tokenizer.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertEqual(tokenizer_slow.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertIsInstance(processor_slow.tokenizer							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.tokenizer							,	__lowerCAmelCase   )

					self.assertEqual(processor_slow.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertEqual(processor_fast.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertIsInstance(processor_slow.image_processor							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.image_processor							,	__lowerCAmelCase   )






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







					"""simple docstring"""






					A__        =   CLIPProcessor(tokenizer=self.get_tokenizer()							,	image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

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

					A__        =   CLIPProcessor.from_pretrained(
					    self.tmpdirname							,	bos_token="""(BOS)"""							,	eos_token="""(EOS)"""							,	do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,	tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,	__lowerCAmelCase   )

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






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







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   self.prepare_image_inputs()

					A__        =   image_processor(__lowerCAmelCase							,	return_tensors="""np"""   )
					A__        =   processor(images=__lowerCAmelCase							,	return_tensors="""np"""   )

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






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







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""

					A__        =   processor(text=__lowerCAmelCase   )

					A__        =   tokenizer(__lowerCAmelCase   )

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






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







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

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

					# test if it raises when no input is passed
					with pytest.raises(__lowerCAmelCase   ):
							processor()






			def    a_       (			self   :    Tuple   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

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

					A__        =   processor.batch_decode(__lowerCAmelCase   )
					A__        =   tokenizer.batch_decode(__lowerCAmelCase   )

					self.assertListEqual(__lowerCAmelCase							,	__lowerCAmelCase   )







			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

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



