





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 					__A   (  unittest.TestCase			):


				def _snake_case				(						self							):
								lowerCamelCase		       =tempfile.mkdtemp()

								lowerCamelCase		       =CLIPImageProcessor()
								lowerCamelCase		       =BertTokenizer.from_pretrained(
								    """hf-internal-testing/tiny-random-BertModel"""      ,       model_input_names=["""input_ids""", """attention_mask"""]							)

								lowerCamelCase		       =GitProcessor(UpperCAmelCase_      ,       UpperCAmelCase_							)

								processor.save_pretrained(self.tmpdirname							)


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCAmelCase_							).tokenizer


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCAmelCase_							).image_processor


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


				def _snake_case				(						self							):
								lowerCamelCase		       =[np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta							)]

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

								return image_inputs


				def _snake_case				(						self							):
								lowerCamelCase		       =GitProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

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

								lowerCamelCase		       =GitProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=UpperCAmelCase_      ,       padding_value=1.0							)

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

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

								lowerCamelCase		       =self.prepare_image_inputs()

								lowerCamelCase		       =image_processor(UpperCAmelCase_      ,       return_tensors="""np"""							)
								lowerCamelCase		       =processor(images=UpperCAmelCase_      ,       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 _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

								lowerCamelCase		       ="""lower newer"""

								lowerCamelCase		       =processor(text=UpperCAmelCase_							)

								lowerCamelCase		       =tokenizer(UpperCAmelCase_      ,       return_token_type_ids=UpperCAmelCase_							)

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

								lowerCamelCase		       =processor(text=UpperCAmelCase_      ,       images=UpperCAmelCase_							)

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

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

								lowerCamelCase		       =processor.batch_decode(UpperCAmelCase_							)
								lowerCamelCase		       =tokenizer.batch_decode(UpperCAmelCase_							)

								self.assertListEqual(UpperCAmelCase_      ,       UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

								lowerCamelCase		       =processor(text=UpperCAmelCase_      ,       images=UpperCAmelCase_							)

								# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
								self.assertListEqual(list(inputs.keys()							)      ,       ["""input_ids""", """attention_mask""", """pixel_values"""]							)












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

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


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}

class 					__A   (  a			):
				__A            = """gpt2"""
				__A            = ["""past_key_values"""]
				__A            = {
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}


				def __init__(						self      ,       UpperCAmelCase_=50257      ,       UpperCAmelCase_=1024      ,       UpperCAmelCase_=768      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="gelu_new"      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=1E-5      ,       UpperCAmelCase_=0.0_2      ,       UpperCAmelCase_="cls_index"      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=False      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =vocab_size
								lowerCamelCase		       =n_positions
								lowerCamelCase		       =n_embd
								lowerCamelCase		       =n_layer
								lowerCamelCase		       =n_head
								lowerCamelCase		       =n_inner
								lowerCamelCase		       =activation_function
								lowerCamelCase		       =resid_pdrop
								lowerCamelCase		       =embd_pdrop
								lowerCamelCase		       =attn_pdrop
								lowerCamelCase		       =layer_norm_epsilon
								lowerCamelCase		       =initializer_range
								lowerCamelCase		       =summary_type
								lowerCamelCase		       =summary_use_proj
								lowerCamelCase		       =summary_activation
								lowerCamelCase		       =summary_first_dropout
								lowerCamelCase		       =summary_proj_to_labels
								lowerCamelCase		       =scale_attn_weights
								lowerCamelCase		       =use_cache
								lowerCamelCase		       =scale_attn_by_inverse_layer_idx
								lowerCamelCase		       =reorder_and_upcast_attn

								lowerCamelCase		       =bos_token_id
								lowerCamelCase		       =eos_token_id

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




class 					__A   (  a			):


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = "default"      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = False      ,       ):
								super().__init__(UpperCAmelCase_      ,       task=UpperCAmelCase_      ,       patching_specs=UpperCAmelCase_      ,       use_past=UpperCAmelCase_							)
								if not getattr(self._config      ,       """pad_token_id"""      ,       UpperCAmelCase_							):
												# TODO: how to do that better?
												lowerCamelCase		       =0


				@property
				def _snake_case				(						self							):
								lowerCamelCase		       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
								if self.use_past:
												self.fill_with_past_key_values_(UpperCAmelCase_      ,       direction="""inputs"""							)
												lowerCamelCase		       ={0: """batch""", 1: """past_sequence + sequence"""}
								else:
												lowerCamelCase		       ={0: """batch""", 1: """sequence"""}

								return common_inputs


				@property
				def _snake_case				(						self							):
								return self._config.n_layer


				@property
				def _snake_case				(						self							):
								return self._config.n_head


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = False      ,       UpperCAmelCase_ = None      ,       ):
								lowerCamelCase		       =super(UpperCAmelCase_      ,       self							).generate_dummy_inputs(
								    UpperCAmelCase_      ,       batch_size=UpperCAmelCase_      ,       seq_length=UpperCAmelCase_      ,       is_pair=UpperCAmelCase_      ,       framework=UpperCAmelCase_							)

								# We need to order the input in the way they appears in the forward()
								lowerCamelCase		       =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

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

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

								return ordered_inputs


				@property
				def _snake_case				(						self							):
								return 13





