






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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




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





	def    lowerCamelCase(self							):
		super().setUp()

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

		A_		:						Optional[int]	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		A_		:						List[Any]	    =   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(lowercase_							) + """\n"""							)
		with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""							) as fp:
			fp.write("""\n""".join(lowercase_							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		kwargs.update(self.special_tokens_map							)
		return CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     **lowercase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		kwargs.update(self.special_tokens_map							)
		return CodeGenTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowercase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   """lower newer"""
		A_		:						Optional[Any]	    =   """lower newer"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						str	    =   CodeGenTokenizer(self.vocab_file     ,     self.merges_file     ,     **self.special_tokens_map							)
		A_		:						Tuple	    =   """lower newer"""
		A_		:						Union[str, Any]	    =   ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
		A_		:						int	    =   tokenizer.tokenize(lowercase_     ,     add_prefix_space=lowercase_							)
		self.assertListEqual(lowercase_     ,     lowercase_							)

		A_		:						Optional[int]	    =   tokens + [tokenizer.unk_token]
		A_		:						Any	    =   [14, 15, 10, 9, 3, 2, 15, 19]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)     ,     lowercase_							)





	def    lowerCamelCase(self							):







		if not self.test_rust_tokenizer:
			return

		A_		:						List[str]	    =   self.get_tokenizer()
		A_		:						List[str]	    =   self.get_rust_tokenizer(add_prefix_space=lowercase_							)

		A_		:						Optional[Any]	    =   """lower newer"""

		# Testing tokenization
		A_		:						List[str]	    =   tokenizer.tokenize(lowercase_     ,     add_prefix_space=lowercase_							)
		A_		:						Any	    =   rust_tokenizer.tokenize(lowercase_							)
		self.assertListEqual(lowercase_     ,     lowercase_							)

		# Testing conversion to ids without special tokens
		A_		:						Optional[int]	    =   tokenizer.encode(lowercase_     ,     add_special_tokens=lowercase_     ,     add_prefix_space=lowercase_							)
		A_		:						Union[str, Any]	    =   rust_tokenizer.encode(lowercase_     ,     add_special_tokens=lowercase_							)
		self.assertListEqual(lowercase_     ,     lowercase_							)

		# Testing conversion to ids with special tokens
		A_		:						List[Any]	    =   self.get_rust_tokenizer(add_prefix_space=lowercase_							)
		A_		:						Any	    =   tokenizer.encode(lowercase_     ,     add_prefix_space=lowercase_							)
		A_		:						Tuple	    =   rust_tokenizer.encode(lowercase_							)
		self.assertListEqual(lowercase_     ,     lowercase_							)

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





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		pass





	def    lowerCamelCase(self     ,     lowerCAmelCase_=15							):







		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
				A_		:						Union[str, Any]	    =   self.rust_tokenizer_class.from_pretrained(lowercase_     ,     **lowercase_							)

				# Simple input
				A_		:						Optional[int]	    =   """This is a simple input"""
				A_		:						str	    =   ["""This is a simple input 1""", """This is a simple input 2"""]
				A_		:						int	    =   ("""This is a simple input""", """This is a pair""")
				A_		:						List[Any]	    =   [
				    ("""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(lowercase_     ,     tokenizer_r.encode     ,     lowercase_     ,     max_length=lowercase_     ,     padding="""max_length"""							)

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

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

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

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

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





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     pad_token="""<pad>"""							)

		# Simple input
		A_		:						Any	    =   """This is a simple input"""
		A_		:						Any	    =   ["""This is a simple input looooooooong""", """This is a simple input"""]
		A_		:						Optional[Any]	    =   ("""This is a simple input""", """This is a pair""")
		A_		:						List[str]	    =   [
		    ("""This is a simple input loooooong""", """This is a simple input"""),
		    ("""This is a simple pair loooooong""", """This is a simple pair"""),
		]

		A_		:						Dict	    =   tokenizer.pad_token_id

		A_		:						Union[str, Any]	    =   tokenizer(lowercase_     ,     padding="""max_length"""     ,     max_length=30     ,     return_tensors="""np"""							)
		A_		:						Any	    =   tokenizer(lowercase_     ,     padding=lowercase_     ,     truncate=lowercase_     ,     return_tensors="""np"""							)
		A_		:						List[str]	    =   tokenizer(*lowercase_     ,     padding="""max_length"""     ,     max_length=60     ,     return_tensors="""np"""							)
		A_		:						int	    =   tokenizer(lowercase_     ,     padding=lowercase_     ,     truncate=lowercase_     ,     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    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   """$$$"""
		A_		:						List[Any]	    =   CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     bos_token=lowercase_     ,     add_bos_token=lowercase_							)

		A_		:						int	    =   """This is a simple input"""
		A_		:						Any	    =   ["""This is a simple input 1""", """This is a simple input 2"""]

		A_		:						str	    =   tokenizer.bos_token_id

		A_		:						Dict	    =   tokenizer(lowercase_							)
		A_		:						Optional[int]	    =   tokenizer(lowercase_							)

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

		A_		:						int	    =   tokenizer.decode(out_s.input_ids							)
		A_		:						str	    =   tokenizer.batch_decode(out_sa.input_ids							)

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





	@slow
	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""							)

		A_		:						List[str]	    =   """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
		A_		:						Any	    =   """\nif len_a > len_b:      result = a\nelse:      result = b"""

		A_		:						int	    =   tokenizer.encode(lowercase_							)
		A_		:						List[Any]	    =   ["""^#""", re.escape("""<|endoftext|>"""							), """^'''""", """^\"\"\"""", """\n\n\n"""]
		A_		:						List[str]	    =   tokenizer.decode(lowercase_     ,     truncate_before_pattern=lowercase_							)
		self.assertEqual(lowercase_     ,     lowercase_							)





	def    lowerCamelCase(self							):
		pass









"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				PriorTransformer
	_A       :				Dict    =				"""hidden_states"""





	@property
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   4
		A_		:						Dict	    =   8
		A_		:						str	    =   7

		A_		:						Dict	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						str	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						List[str]	    =   floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   4
		A_		:						Tuple	    =   8
		A_		:						Tuple	    =   7

		A_		:						int	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						List[str]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	def    lowerCamelCase(self							):
		A_		:						int	    =   {
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 4,
		    """num_layers""": 2,
		    """embedding_dim""": 8,
		    """num_embeddings""": 7,
		    """additional_embeddings""": 4,
		}
		A_		:						Dict	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Optional[int]	    =   PriorTransformer.from_pretrained(
		    """hf-internal-testing/prior-dummy"""     ,     output_loading_info=lowerCAmelCase_							)
		self.assertIsNotNone(lowerCAmelCase_							)
		self.assertEqual(len(loading_info["""missing_keys"""]							)     ,     0							)

		model.to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   model(**self.dummy_input							)[0]

		assert hidden_states is not None, "Make sure output is not None"





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()

		A_		:						Union[str, Any]	    =   self.model_class(**lowerCAmelCase_							)
		A_		:						Tuple	    =   inspect.signature(model.forward							)
		# signature.parameters is an OrderedDict => so arg_names order is deterministic
		A_		:						Tuple	    =   [*signature.parameters.keys()]

		A_		:						List[str]	    =   ["""hidden_states""", """timestep"""]
		self.assertListEqual(arg_names[:2]     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""							)
		A_		:						Optional[Any]	    =   model.to(lowerCAmelCase_							)

		if hasattr(lowerCAmelCase_     ,     """set_default_attn_processor"""							):
			model.set_default_attn_processor()

		A_		:						int	    =   self.get_dummy_seed_input()

		with torch.no_grad():
			A_		:						int	    =   model(**lowerCAmelCase_							)[0]

		A_		:						Any	    =   output[0, :5].flatten().cpu()
		print(lowerCAmelCase_							)

		# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
		# the expected output slices are not the same for CPU and GPU.
		A_		:						Optional[int]	    =   torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]							)
		self.assertTrue(torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     rtol=1e-2							)							)






@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=77     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Tuple	    =   batch_size
		A_		:						Any	    =   embedding_dim
		A_		:						Tuple	    =   num_embeddings

		A_		:						Any	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	def    lowerCamelCase(self							):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
	        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
	        # fmt: on
	    ]							)
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""     ,     subfolder="""prior"""							)
		model.to(lowerCAmelCase_							)
		A_		:						int	    =   self.get_dummy_seed_input(seed=lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						Tuple	    =   model(**lowerCAmelCase_							)[0]

		assert list(sample.shape							) == [1, 768]

		A_		:						Tuple	    =   sample[0, :8].flatten().cpu()
		print(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(lowerCAmelCase_							)

		assert torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     atol=1e-3							)

