




"""simple docstring"""







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						RobertaTokenizer
						_a    						=						RobertaTokenizerFast
						_a    						=						True
						_a    						=						{'cls_token': '<s>'}


						def   __lowercase (							self				:			str					):
										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>""",
										]
										lowerCAmelCase  =				dict(zip(lowerCAmelCase						,						range(len(lowerCAmelCase					)					)					)					)
										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(lowerCAmelCase					) + """\n"""					)
										with open(self.merges_file						,						"""w"""						,						encoding="""utf-8"""					) as fp:
														fp.write("""\n""".join(lowerCAmelCase					)					)


						def   __lowercase (							self				:			Optional[Any]						,						**lowerCAmelCase				:			List[Any]					):
										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**lowerCAmelCase					)


						def   __lowercase (							self				:			Dict						,						**lowerCAmelCase				:			List[str]					):
										kwargs.update(self.special_tokens_map					)
										return RobertaTokenizerFast.from_pretrained(self.tmpdirname						,						**lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Dict					):
										lowerCAmelCase  =				"""lower newer"""
										lowerCAmelCase  =				"""lower newer"""
										return input_text, output_text


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.tokenizer_class(self.vocab_file						,						self.merges_file						,						**self.special_tokens_map					)
										lowerCAmelCase  =				"""lower newer"""
										lowerCAmelCase  =				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
										lowerCAmelCase  =				tokenizer.tokenize(lowerCAmelCase					)  # , add_prefix_space=True)
										self.assertListEqual(lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				tokens + [tokenizer.unk_token]
										lowerCAmelCase  =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase					)						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("""Hello world!"""						,						add_special_tokens=lowerCAmelCase					)						,						[0, 3_1414, 232, 328, 2]					)
										self.assertListEqual(
										    tokenizer.encode("""Hello world! cécé herlolip 418"""						,						add_special_tokens=lowerCAmelCase					)						,						[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]						,						)


						@slow
						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

										lowerCAmelCase  =				tokenizer.encode("""sequence builders"""						,						add_special_tokens=lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.encode("""multi-sequence build"""						,						add_special_tokens=lowerCAmelCase					)

										lowerCAmelCase  =				tokenizer.encode(
										    """sequence builders"""						,						add_special_tokens=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.encode(
										    """sequence builders"""						,						"""multi-sequence build"""						,						add_special_tokens=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase					)

										lowerCAmelCase  =				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase						,						lowerCAmelCase					)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.get_tokenizer()

										lowerCAmelCase  =				"""Encode this sequence."""
										lowerCAmelCase  =				tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

										# Testing encoder arguments
										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
										self.assertNotEqual(lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
										self.assertEqual(lowerCAmelCase						,						lowerCAmelCase					)

										tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
										self.assertNotEqual(lowerCAmelCase						,						lowerCAmelCase					)

										# Testing spaces after special tokens
										lowerCAmelCase  =				"""<mask>"""
										tokenizer.add_special_tokens(
										    {"""mask_token""": AddedToken(lowerCAmelCase						,						lstrip=lowerCAmelCase						,						rstrip=lowerCAmelCase					)}					)  # mask token has a left space
										lowerCAmelCase  =				tokenizer.convert_tokens_to_ids(lowerCAmelCase					)

										lowerCAmelCase  =				"""Encode <mask> sequence"""
										lowerCAmelCase  =				"""Encode <mask>sequence"""

										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase					)
										lowerCAmelCase  =				encoded.index(lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
										self.assertEqual(lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				tokenizer.encode(lowerCAmelCase					)
										lowerCAmelCase  =				encoded.index(lowerCAmelCase					)
										lowerCAmelCase  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
										self.assertNotEqual(lowerCAmelCase						,						lowerCAmelCase					)


						def   __lowercase (							self				:			List[str]					):
										pass


						def   __lowercase (							self				:			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(lowerCAmelCase						,						**lowerCAmelCase					)
																		lowerCAmelCase  =				self.tokenizer_class.from_pretrained(lowerCAmelCase						,						**lowerCAmelCase					)
																		lowerCAmelCase  =				"""A, <mask> AllenNLP sentence."""
																		lowerCAmelCase  =				tokenizer_r.encode_plus(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						return_token_type_ids=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_p.encode_plus(lowerCAmelCase						,						add_special_tokens=lowerCAmelCase						,						return_token_type_ids=lowerCAmelCase					)

																		# token_type_ids should put 0 everywhere
																		self.assertEqual(sum(tokens_r["""token_type_ids"""]					)						,						sum(tokens_p["""token_type_ids"""]					)					)

																		# attention_mask should put 1 everywhere, so sum over length should be 1
																		self.assertEqual(
																		    sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)						,						sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)						,						)

																		lowerCAmelCase  =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
																		lowerCAmelCase  =				tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

																		# Rust correctly handles the space before the mask while python doesnt
																		self.assertSequenceEqual(tokens_p["""input_ids"""]						,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
																		self.assertSequenceEqual(tokens_r["""input_ids"""]						,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

																		self.assertSequenceEqual(
																		    lowerCAmelCase						,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
																		self.assertSequenceEqual(
																		    lowerCAmelCase						,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)


						def   __lowercase (							self				:			Optional[Any]					):
										for trim_offsets, add_prefix_space in itertools.product([True, False]						,						repeat=2					):
														lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)

														lowerCAmelCase  =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
														lowerCAmelCase  =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

														self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]						,						lowerCAmelCase					)

														self.assertEqual(post_processor_state["""add_prefix_space"""]						,						lowerCAmelCase					)
														self.assertEqual(post_processor_state["""trim_offsets"""]						,						lowerCAmelCase					)




						def   __lowercase (							self				:			Optional[int]					):
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
										# `trim_offsets`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																		lowerCAmelCase  =				"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		lowerCAmelCase  =				f'''{text_of_1_token} {text_of_1_token}'''

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(len(lowerCAmelCase					) + 1, len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(len(lowerCAmelCase					) + 1, len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(len(lowerCAmelCase					), len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(len(lowerCAmelCase					), len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				f''' {text}'''

																		# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																		#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																		# )
																		# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																		# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																		# self.assertEqual(
																		#     encoding.offset_mapping[1],
																		#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																		# )

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(1, 1 + len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(1 + len(lowerCAmelCase					) + 1, 1 + len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, 1 + len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(1 + len(lowerCAmelCase					), 1 + len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)

																		lowerCAmelCase  =				self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase						,						use_fast=lowerCAmelCase						,						add_prefix_space=lowerCAmelCase						,						trim_offsets=lowerCAmelCase					)
																		lowerCAmelCase  =				tokenizer_r(lowerCAmelCase						,						return_offsets_mapping=lowerCAmelCase						,						add_special_tokens=lowerCAmelCase					)
																		self.assertEqual(encoding.offset_mapping[0]						,						(0, 1 + len(lowerCAmelCase					))					)
																		self.assertEqual(
																		    encoding.offset_mapping[1]						,						(1 + len(lowerCAmelCase					), 1 + len(lowerCAmelCase					) + 1 + len(lowerCAmelCase					))						,						)











"""simple docstring"""







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=lowerCAmelCase						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.out_channels						,						n_heads=self.num_attention_heads						,						d_head=self.out_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						only_cross_attention=self.only_cross_attention						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

										if self.add_downsample:
														lowerCAmelCase  =				FlaxDownsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			int=True					):
										lowerCAmelCase  =				()

										for resnet, attn in zip(self.resnets						,						self.attentions					):
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														output_states += (hidden_states,)

										if self.add_downsample:
														lowerCAmelCase  =				self.downsamplers_a(lowerCAmelCase					)
														output_states += (hidden_states,)

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=lowerCAmelCase						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)
										lowerCAmelCase  =				resnets

										if self.add_downsample:
														lowerCAmelCase  =				FlaxDownsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			int						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			List[str]=True					):
										lowerCAmelCase  =				()

										for resnet in self.resnets:
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														output_states += (hidden_states,)

										if self.add_downsample:
														lowerCAmelCase  =				self.downsamplers_a(lowerCAmelCase					)
														output_states += (hidden_states,)

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if (i == self.num_layers - 1) else self.out_channels
														lowerCAmelCase  =				self.prev_output_channel if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.out_channels						,						n_heads=self.num_attention_heads						,						d_head=self.out_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						only_cross_attention=self.only_cross_attention						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

										if self.add_upsample:
														lowerCAmelCase  =				FlaxUpsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Dict=True					):
										for resnet, attn in zip(self.resnets						,						self.attentions					):
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				[]

										for i in range(self.num_layers					):
														lowerCAmelCase  =				self.in_channels if (i == self.num_layers - 1) else self.out_channels
														lowerCAmelCase  =				self.prev_output_channel if i == 0 else self.out_channels

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=resnet_in_channels + res_skip_channels						,						out_channels=self.out_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets

										if self.add_upsample:
														lowerCAmelCase  =				FlaxUpsampleaD(self.out_channels						,						dtype=self.dtype					)




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Dict=True					):
										for resnet in self.resnets:
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[Any]					):
										# there is always at least one resnet
										lowerCAmelCase  =				[
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels						,						out_channels=self.in_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
										]

										lowerCAmelCase  =				[]

										for _ in range(self.num_layers					):
														lowerCAmelCase  =				FlaxTransformeraDModel(
														    in_channels=self.in_channels						,						n_heads=self.num_attention_heads						,						d_head=self.in_channels // self.num_attention_heads						,						depth=1						,						use_linear_projection=self.use_linear_projection						,						use_memory_efficient_attention=self.use_memory_efficient_attention						,						dtype=self.dtype						,						)
														attentions.append(lowerCAmelCase					)

														lowerCAmelCase  =				FlaxResnetBlockaD(
														    in_channels=self.in_channels						,						out_channels=self.in_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
														resnets.append(lowerCAmelCase					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions




						def __call__(							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]=True					):
										lowerCAmelCase  =				self.resnets[0](lowerCAmelCase						,						lowerCAmelCase					)
										for attn, resnet in zip(self.attentions						,						self.resnets[1:]					):
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										return hidden_states





