
import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel







class 				lowercase		:




							def __init__(				self   ,A__   ,A__=1_3   ,A__=7   ,A__=True   ,A__=True   ,A__=True   ,A__=True   ,A__=9_9   ,A__=3_2   ,A__=5   ,A__=4   ,A__=4   ,A__="gelu"   ,A__=0.0   ,A__=0.1   ,A__=True   ,A__=5_1_2   ,A__=1_6   ,A__=2   ,A__=0.02   ,A__=3   ,A__=4   ,A__=None   ,):
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						seq_length
												lowercase							=						is_training
												lowercase							=						use_input_mask
												lowercase							=						use_token_type_ids
												lowercase							=						use_labels
												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						intermediate_multiple_size
												lowercase							=						hidden_act
												lowercase							=						hidden_dropout
												lowercase							=						attention_dropout
												lowercase							=						weight_tying
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						type_sequence_label_size
												lowercase							=						initializer_range
												lowercase							=						num_labels
												lowercase							=						num_choices
												lowercase							=						scope




							def  A__      (				self):
												lowercase							=						ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size)

												lowercase							=						None
												if self.use_input_mask:
																	lowercase							=						random_attention_mask([self.batch_size, self.seq_length])

												lowercase							=						None
												if self.use_labels:
																	lowercase							=						ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels)

												lowercase							=						self.get_config()

												return config, input_ids, input_mask, token_labels




							def  A__      (				self):
												return GPTNeoXJapaneseConfig(
												    vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_multiple_size=self.intermediate_multiple_size   ,hidden_act=self.hidden_act   ,hidden_dropout=self.hidden_dropout   ,attention_dropout=self.attention_dropout   ,weight_tying=self.weight_tying   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,is_decoder=A__   ,initializer_range=self.initializer_range   ,)




							def  A__      (				self):
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						self.prepare_config_and_inputs()

												lowercase							=						True

												return config, input_ids, input_mask, token_labels




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						GPTNeoXJapaneseModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__   ,attention_mask=A__)
												lowercase							=						model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						True
												lowercase							=						GPTNeoXJapaneseModel(A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__   ,attention_mask=A__)
												self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size))




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						GPTNeoXJapaneseForCausalLM(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						model(A__   ,attention_mask=A__   ,labels=A__)
												self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size))




							def  A__      (				self   ,A__   ,A__   ,A__):
												lowercase							=						True
												lowercase							=						GPTNeoXJapaneseForCausalLM(config=A__)
												model.to(A__)
												model.eval()

												# first forward pass
												lowercase							=						model(A__   ,attention_mask=A__   ,use_cache=A__)
												lowercase							=						outputs.past_key_values

												# create hypothetical multiple next token and extent to next_input_ids
												lowercase							=						ids_tensor((self.batch_size, 3)   ,config.vocab_size)
												lowercase							=						ids_tensor((self.batch_size, 3)   ,vocab_size=2)

												# append to next input_ids and
												lowercase							=						torch.cat([input_ids, next_tokens]   ,dim=-1)
												lowercase							=						torch.cat([input_mask, next_mask]   ,dim=-1)

												lowercase							=						model(A__   ,attention_mask=A__   ,output_hidden_states=A__)
												lowercase							=						output_from_no_past['''hidden_states'''][0]
												lowercase							=						model(
												    A__   ,attention_mask=A__   ,past_key_values=A__   ,output_hidden_states=A__   ,)['''hidden_states'''][0]

												# select random slice
												lowercase							=						ids_tensor((1,)   ,output_from_past.shape[-1]).item()
												lowercase							=						output_from_no_past[:, -3:, random_slice_idx].detach()
												lowercase							=						output_from_past[:, :, random_slice_idx].detach()

												self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

												# test that outputs are equal for slice
												self.parent.assertTrue(torch.allclose(A__   ,A__   ,atol=1E-3))





							def  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						config_and_inputs
												lowercase							=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_torch
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
							lowercase_       :       List[Any]       					=(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
							lowercase_       :       Tuple       					=(
							    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
							    if is_torch_available()
							    else {}
							)
							lowercase_       :       str       					=False
							lowercase_       :       Optional[Any]       					=False
							lowercase_       :       List[str]       					=False
							lowercase_       :       Optional[int]       					=False




							def  A__      (				self):
												lowercase							=						GPTNeoXJapaneseModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=A__   ,hidden_size=3_7)




							def  A__      (				self):
												self.config_tester.run_common_tests()




							def  A__      (				self):
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(A__   ,A__   ,A__)




							def  A__      (				self):
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(A__   ,A__   ,A__)




							def  A__      (				self):
												# This regression test was failing with PyTorch < 1.3
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs_for_decoder()

												lowercase							=						None

												self.model_tester.create_and_check_model_as_decoder(A__   ,A__   ,A__)




							def  A__      (				self):
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(A__   ,A__   ,A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*A__)





							@slow
							def  A__      (				self):
												lowercase							=						'''abeja/gpt-neox-japanese-2.7b'''

												lowercase							=						['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

												lowercase							=						[
												    '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
												    '''100年後に必要とされる会社は、「人」が中心の会社です。''',
												    '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
												    '''国境の長いトンネルを抜けると、そこは雪国だった。''',
												    '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
												]

												lowercase							=						GPTNeoXJapaneseTokenizer.from_pretrained(A__)
												lowercase							=						GPTNeoXJapaneseForCausalLM.from_pretrained(A__)

												lowercase							=						[]
												for prompt in prompts:
																	lowercase							=						tokenizer(A__   ,return_tensors='''pt''').input_ids
																	lowercase							=						model.generate(A__   ,max_length=5_0)
																	lowercase							=						tokenizer.batch_decode(A__   ,skip_special_tokens=A__)
																	predicted_outputs += generated_string
												self.assertListEqual(A__   ,A__)






import copy
import os
from typing import Union

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


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	lowercase							=						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls   ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__   ,**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@classmethod
							def  A__      (				cls   ,A__   ,A__   ,**A__):
												return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**A__)





							def  A__      (				self):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output




