



'''simple docstring'''

import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Tuple									=		CodeGenTokenizer
						UpperCAmelCase							:   List[str]									=		CodeGenTokenizerFast
						UpperCAmelCase							:   Optional[Any]									=		True
						UpperCAmelCase							:   Optional[int]									=		{"add_prefix_space": True}
						UpperCAmelCase							:   List[str]									=		False


						def     __snake_case   ( self				)     ->     Optional[Any]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCAmelCase   =     [
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							    """<|endoftext|>""",
							]
							lowerCAmelCase   =     dict(zip(A_     ,	range(len(A_				)				)				)				)
							lowerCAmelCase   =     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							lowerCAmelCase   =     {"""unk_token""": """<unk>"""}

							lowerCAmelCase   =     os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES["""vocab_file"""]				)
							lowerCAmelCase   =     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_				)				)


						def     __snake_case   ( self     ,	**A_				)     ->     Optional[int]:
							kwargs.update(self.special_tokens_map				)
							return CodeGenTokenizer.from_pretrained(self.tmpdirname     ,	**A_				)


						def     __snake_case   ( self     ,	**A_				)     ->     List[Any]:
							kwargs.update(self.special_tokens_map				)
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname     ,	**A_				)


						def     __snake_case   ( self     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     """lower newer"""
							lowerCAmelCase   =     """lower newer"""
							return input_text, output_text


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     CodeGenTokenizer(self.vocab_file     ,	self.merges_file     ,	**self.special_tokens_map				)
							lowerCAmelCase   =     """lower newer"""
							lowerCAmelCase   =     ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
							lowerCAmelCase   =     tokenizer.tokenize(A_     ,	add_prefix_space=A_				)
							self.assertListEqual(A_     ,	A_				)

							lowerCAmelCase   =     tokens + [tokenizer.unk_token]
							lowerCAmelCase   =     [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_				)     ,	A_				)


						def     __snake_case   ( self				)     ->     Dict:
							if not self.test_rust_tokenizer:
								return

							lowerCAmelCase   =     self.get_tokenizer()
							lowerCAmelCase   =     self.get_rust_tokenizer(add_prefix_space=A_				)

							lowerCAmelCase   =     """lower newer"""

							# Testing tokenization
							lowerCAmelCase   =     tokenizer.tokenize(A_     ,	add_prefix_space=A_				)
							lowerCAmelCase   =     rust_tokenizer.tokenize(A_				)
							self.assertListEqual(A_     ,	A_				)

							# Testing conversion to ids without special tokens
							lowerCAmelCase   =     tokenizer.encode(A_     ,	add_special_tokens=A_     ,	add_prefix_space=A_				)
							lowerCAmelCase   =     rust_tokenizer.encode(A_     ,	add_special_tokens=A_				)
							self.assertListEqual(A_     ,	A_				)

							# Testing conversion to ids with special tokens
							lowerCAmelCase   =     self.get_rust_tokenizer(add_prefix_space=A_				)
							lowerCAmelCase   =     tokenizer.encode(A_     ,	add_prefix_space=A_				)
							lowerCAmelCase   =     rust_tokenizer.encode(A_				)
							self.assertListEqual(A_     ,	A_				)

							# Testing the unknown token
							lowerCAmelCase   =     tokens + [rust_tokenizer.unk_token]
							lowerCAmelCase   =     [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_				)     ,	A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							# It's very difficult to mix/test pretokenization with byte-level
							# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
							pass


						def     __snake_case   ( self     ,	A_=15				)     ->     Optional[Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'				):
									lowerCAmelCase   =     self.rust_tokenizer_class.from_pretrained(A_     ,	**A_				)

									# Simple input
									lowerCAmelCase   =     """This is a simple input"""
									lowerCAmelCase   =     ["""This is a simple input 1""", """This is a simple input 2"""]
									lowerCAmelCase   =     ("""This is a simple input""", """This is a pair""")
									lowerCAmelCase   =     [
									    ("""This is a simple input 1""", """This is a simple input 2"""),
									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
									]

									# Simple input tests
									self.assertRaises(A_     ,	tokenizer_r.encode     ,	A_     ,	max_length=A_     ,	padding="""max_length"""				)

									# Simple input
									self.assertRaises(A_     ,	tokenizer_r.encode_plus     ,	A_     ,	max_length=A_     ,	padding="""max_length"""				)

									# Simple input
									self.assertRaises(
									    A_     ,	tokenizer_r.batch_encode_plus     ,	A_     ,	max_length=A_     ,	padding="""max_length"""     ,	)

									# Pair input
									self.assertRaises(A_     ,	tokenizer_r.encode     ,	A_     ,	max_length=A_     ,	padding="""max_length"""				)

									# Pair input
									self.assertRaises(A_     ,	tokenizer_r.encode_plus     ,	A_     ,	max_length=A_     ,	padding="""max_length"""				)

									# Pair input
									self.assertRaises(
									    A_     ,	tokenizer_r.batch_encode_plus     ,	A_     ,	max_length=A_     ,	padding="""max_length"""     ,	)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     CodeGenTokenizer.from_pretrained(self.tmpdirname     ,	pad_token="""<pad>"""				)

							# Simple input
							lowerCAmelCase   =     """This is a simple input"""
							lowerCAmelCase   =     ["""This is a simple input looooooooong""", """This is a simple input"""]
							lowerCAmelCase   =     ("""This is a simple input""", """This is a pair""")
							lowerCAmelCase   =     [
							    ("""This is a simple input loooooong""", """This is a simple input"""),
							    ("""This is a simple pair loooooong""", """This is a simple pair"""),
							]

							lowerCAmelCase   =     tokenizer.pad_token_id

							lowerCAmelCase   =     tokenizer(A_     ,	padding="""max_length"""     ,	max_length=30     ,	return_tensors="""np"""				)
							lowerCAmelCase   =     tokenizer(A_     ,	padding=A_     ,	truncate=A_     ,	return_tensors="""np"""				)
							lowerCAmelCase   =     tokenizer(*A_     ,	padding="""max_length"""     ,	max_length=60     ,	return_tensors="""np"""				)
							lowerCAmelCase   =     tokenizer(A_     ,	padding=A_     ,	truncate=A_     ,	return_tensors="""np"""				)

							# s
							# test single string max_length padding
							self.assertEqual(out_s["""input_ids"""].shape[-1]     ,	30				)
							self.assertTrue(pad_token_id in out_s["""input_ids"""]				)
							self.assertTrue(0 in out_s["""attention_mask"""]				)

							# s2
							# test automatic padding
							self.assertEqual(out_sa["""input_ids"""].shape[-1]     ,	33				)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]				)
							self.assertFalse(0 in out_sa["""attention_mask"""][0]				)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]				)
							self.assertTrue(0 in out_sa["""attention_mask"""][1]				)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p["""input_ids"""].shape[-1]     ,	60				)
							self.assertTrue(pad_token_id in out_p["""input_ids"""]				)
							self.assertTrue(0 in out_p["""attention_mask"""]				)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa["""input_ids"""].shape[-1]     ,	52				)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]				)
							self.assertFalse(0 in out_pa["""attention_mask"""][0]				)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]				)
							self.assertTrue(0 in out_pa["""attention_mask"""][1]				)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     """$$$"""
							lowerCAmelCase   =     CodeGenTokenizer.from_pretrained(self.tmpdirname     ,	bos_token=A_     ,	add_bos_token=A_				)

							lowerCAmelCase   =     """This is a simple input"""
							lowerCAmelCase   =     ["""This is a simple input 1""", """This is a simple input 2"""]

							lowerCAmelCase   =     tokenizer.bos_token_id

							lowerCAmelCase   =     tokenizer(A_				)
							lowerCAmelCase   =     tokenizer(A_				)

							self.assertEqual(out_s.input_ids[0]     ,	A_				)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

							lowerCAmelCase   =     tokenizer.decode(out_s.input_ids				)
							lowerCAmelCase   =     tokenizer.batch_decode(out_sa.input_ids				)

							self.assertEqual(decode_s.split()[0]     ,	A_				)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)


						@slow
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""				)

							lowerCAmelCase   =     """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
							lowerCAmelCase   =     """\nif len_a > len_b:      result = a\nelse:      result = b"""

							lowerCAmelCase   =     tokenizer.encode(A_				)
							lowerCAmelCase   =     ["""^#""", re.escape("""<|endoftext|>"""				), """^'''""", """^\"\"\"""", """\n\n\n"""]
							lowerCAmelCase   =     tokenizer.decode(A_     ,	truncate_before_pattern=A_				)
							self.assertEqual(A_     ,	A_				)




						def     __snake_case   ( self				)     ->     Tuple:
							pass




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)