
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 CLIPSegProcessor, ViTImageProcessor







@require_vision
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												lowercase						     =						tempfile.mkdtemp()

												# fmt: off
												lowercase						     =						['''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
												lowercase						     =						dict(zip(A__       ,range(len(A__))))
												lowercase						     =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
												lowercase						     =						{'''unk_token''': '''<unk>'''}

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

												lowercase						     =						{
												    '''do_resize''': True,
												    '''size''': 2_0,
												    '''do_center_crop''': True,
												    '''crop_size''': 1_8,
												    '''do_normalize''': True,
												    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
												    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
												}
												lowercase						     =						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 CLIPTokenizer.from_pretrained(self.tmpdirname       ,**A__)




							def 			A__				(    self       ,**A__):
												return CLIPTokenizerFast.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):
												lowercase						     =						[np.random.randint(2_5_5       ,size=(3, 3_0, 4_0_0)       ,dtype=np.uinta)]

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

												return image_inputs




							def 			A__				(    self):
												lowercase						     =						self.get_tokenizer()
												lowercase						     =						self.get_rust_tokenizer()
												lowercase						     =						self.get_image_processor()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)
												processor_slow.save_pretrained(self.tmpdirname)
												lowercase						     =						CLIPSegProcessor.from_pretrained(self.tmpdirname       ,use_fast=A__)

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)
												processor_fast.save_pretrained(self.tmpdirname)
												lowercase						     =						CLIPSegProcessor.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       ,A__)
												self.assertIsInstance(processor_fast.tokenizer       ,A__)

												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       ,A__)
												self.assertIsInstance(processor_fast.image_processor       ,A__)




							def 			A__				(    self):
												lowercase						     =						CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor())
												processor.save_pretrained(self.tmpdirname)

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

												lowercase						     =						CLIPSegProcessor.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       ,A__)

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




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)

												lowercase						     =						self.prepare_image_inputs()

												lowercase						     =						image_processor(A__       ,return_tensors='''np''')
												lowercase						     =						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):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)

												lowercase						     =						'''lower newer'''

												lowercase						     =						processor(text=A__)

												lowercase						     =						tokenizer(A__)

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




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)

												lowercase						     =						'''lower newer'''
												lowercase						     =						self.prepare_image_inputs()

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

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

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




							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)

												lowercase						     =						self.prepare_image_inputs()
												lowercase						     =						self.prepare_image_inputs()

												lowercase						     =						processor(images=A__       ,visual_prompt=A__)

												self.assertListEqual(list(inputs.keys())       ,['''pixel_values''', '''conditional_pixel_values'''])

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





							def 			A__				(    self):
												lowercase						     =						self.get_image_processor()
												lowercase						     =						self.get_tokenizer()

												lowercase						     =						CLIPSegProcessor(tokenizer=A__       ,image_processor=A__)

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

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

												self.assertListEqual(A__       ,A__)






import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class     lowercase		:




							def __init__(    self       ,A__       ,A__=9_9       ,A__=1_3       ,A__=1_6       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=False       ,A__=True       ,A__=2       ,A__=3_2       ,A__=4       ,A__=4       ,A__=3_0       ,A__=0       ,A__=1       ,A__=2       ,A__=None       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						decoder_seq_length
												# For common tests
												lowercase						     =						self.decoder_seq_length
												lowercase						     =						is_training
												lowercase						     =						use_attention_mask
												lowercase						     =						use_labels

												lowercase						     =						vocab_size
												lowercase						     =						d_model
												lowercase						     =						d_model
												lowercase						     =						decoder_layers
												lowercase						     =						decoder_layers
												lowercase						     =						decoder_ffn_dim
												lowercase						     =						decoder_attention_heads
												lowercase						     =						decoder_attention_heads
												lowercase						     =						eos_token_id
												lowercase						     =						bos_token_id
												lowercase						     =						pad_token_id
												lowercase						     =						decoder_start_token_id
												lowercase						     =						use_cache
												lowercase						     =						max_position_embeddings

												lowercase						     =						None
												lowercase						     =						decoder_seq_length
												lowercase						     =						2
												lowercase						     =						1




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

												lowercase						     =						None
												if self.use_attention_mask:
																	lowercase						     =						ids_tensor([self.batch_size, self.decoder_seq_length]       ,vocab_size=2)

												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size, self.decoder_seq_length]       ,self.vocab_size)

												lowercase						     =						TrOCRConfig(
												    vocab_size=self.vocab_size       ,d_model=self.d_model       ,decoder_layers=self.decoder_layers       ,decoder_ffn_dim=self.decoder_ffn_dim       ,decoder_attention_heads=self.decoder_attention_heads       ,eos_token_id=self.eos_token_id       ,bos_token_id=self.bos_token_id       ,use_cache=self.use_cache       ,pad_token_id=self.pad_token_id       ,decoder_start_token_id=self.decoder_start_token_id       ,max_position_embeddings=self.max_position_embeddings       ,)

												return (config, input_ids, attention_mask, lm_labels)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,):
												lowercase						     =						True
												lowercase						     =						TrOCRDecoder(config=A__).to(A__).eval()
												lowercase						     =						input_ids[:2]

												input_ids[input_ids == 0] += 1
												# first forward pass
												lowercase						     =						model(A__       ,use_cache=A__)
												lowercase						     =						model(A__)
												lowercase						     =						model(A__       ,use_cache=A__)

												self.parent.assertTrue(len(A__) == len(A__))
												self.parent.assertTrue(len(A__) == len(A__) + 1)

												lowercase						     =						outputs['''past_key_values''']

												# create hypothetical next token and extent to next_input_ids
												lowercase						     =						ids_tensor((2, 1)       ,config.vocab_size - 1) + 1

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

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						model(A__       ,past_key_values=A__)['''last_hidden_state''']

												# select random slice
												lowercase						     =						ids_tensor((1,)       ,output_from_past.shape[-1]).item()
												lowercase						     =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
												lowercase						     =						output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												assert 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''': attention_mask}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Any	 =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
							lowercase_	:	Dict	 =(TrOCRForCausalLM,) if is_torch_available() else ()
							lowercase_	:	int	 ={'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
							lowercase_	:	List[Any]	 =True
							lowercase_	:	int	 =False




							def 			A__				(    self):
												lowercase						     =						TrOCRStandaloneDecoderModelTester(self       ,is_training=A__)
												lowercase						     =						ConfigTester(self       ,config_class=A__)




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												pass




							def 			A__				(    self):
												pass




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




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




							def 			A__				(    self):
												return





							@unittest.skip('''The model doesn\'t support left padding''')  # and it's not used enough to be worth fixing :)
							def 			A__				(    self):
												pass




