'''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 					__magic_name__		(					nn.Module):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 0.0
			UpperCamelCase__       = 1
			UpperCamelCase__       = 1
			UpperCamelCase__       = True
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = jnp.floataa







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Union[str, Any]       		= []
										lowercase_      :    Union[str, Any]       		= []

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

																	lowercase_      :    Tuple       		= FlaxResnetBlockaD(
																	    in_channels=lowercase_       ,    out_channels=self.out_channels       ,    dropout_prob=self.dropout       ,    dtype=self.dtype       ,    )
																	resnets.append(lowercase_					)

																	lowercase_      :    Any       		= 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(lowercase_					)

										lowercase_      :    Union[str, Any]       		= resnets
										lowercase_      :    Any       		= attentions

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







			def __call__(			self       :						Union[str, Any]       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Dict       ,    lowercase_       :						Optional[Any]=True					):
										lowercase_      :    int       		= ()

										for resnet, attn in zip(self.resnets       ,    self.attentions					):
																	lowercase_      :    Optional[Any]       		= resnet(lowercase_       ,    lowercase_       ,    deterministic=lowercase_					)
																	lowercase_      :    Tuple       		= attn(lowercase_       ,    lowercase_       ,    deterministic=lowercase_					)
																	output_states += (hidden_states,)

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

										return hidden_states, output_states





class 					__magic_name__		(					nn.Module):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 0.0
			UpperCamelCase__       = 1
			UpperCamelCase__       = True
			UpperCamelCase__       = jnp.floataa







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= []

										for i in range(self.num_layers					):
																	lowercase_      :    Union[str, Any]       		= self.in_channels if i == 0 else self.out_channels

																	lowercase_      :    str       		= FlaxResnetBlockaD(
																	    in_channels=lowercase_       ,    out_channels=self.out_channels       ,    dropout_prob=self.dropout       ,    dtype=self.dtype       ,    )
																	resnets.append(lowercase_					)
										lowercase_      :    str       		= resnets

										if self.add_downsample:
																	lowercase_      :    Optional[int]       		= FlaxDownsampleaD(self.out_channels       ,    dtype=self.dtype					)







			def __call__(			self       :						Dict       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Optional[Any]=True					):
										lowercase_      :    int       		= ()

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

										if self.add_downsample:
																	lowercase_      :    List[str]       		= self.downsamplers_a(lowercase_					)
																	output_states += (hidden_states,)

										return hidden_states, output_states





class 					__magic_name__		(					nn.Module):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 0.0
			UpperCamelCase__       = 1
			UpperCamelCase__       = 1
			UpperCamelCase__       = True
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = jnp.floataa







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Tuple       		= []
										lowercase_      :    str       		= []

										for i in range(self.num_layers					):
																	lowercase_      :    List[str]       		= self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	lowercase_      :    Union[str, Any]       		= self.prev_output_channel if i == 0 else self.out_channels

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

																	lowercase_      :    str       		= 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(lowercase_					)

										lowercase_      :    List[Any]       		= resnets
										lowercase_      :    Dict       		= attentions

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







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

																	lowercase_      :    Tuple       		= resnet(lowercase_       ,    lowercase_       ,    deterministic=lowercase_					)
																	lowercase_      :    Union[str, Any]       		= attn(lowercase_       ,    lowercase_       ,    deterministic=lowercase_					)

										if self.add_upsample:
																	lowercase_      :    Union[str, Any]       		= self.upsamplers_a(lowercase_					)

										return hidden_states





class 					__magic_name__		(					nn.Module):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42
			UpperCamelCase__       = 0.0
			UpperCamelCase__       = 1
			UpperCamelCase__       = True
			UpperCamelCase__       = jnp.floataa







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Optional[int]       		= []

										for i in range(self.num_layers					):
																	lowercase_      :    List[str]       		= self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	lowercase_      :    List[Any]       		= self.prev_output_channel if i == 0 else self.out_channels

																	lowercase_      :    Union[str, Any]       		= FlaxResnetBlockaD(
																	    in_channels=resnet_in_channels + res_skip_channels       ,    out_channels=self.out_channels       ,    dropout_prob=self.dropout       ,    dtype=self.dtype       ,    )
																	resnets.append(lowercase_					)

										lowercase_      :    Dict       		= resnets

										if self.add_upsample:
																	lowercase_      :    List[Any]       		= FlaxUpsampleaD(self.out_channels       ,    dtype=self.dtype					)







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

																	lowercase_      :    Union[str, Any]       		= resnet(lowercase_       ,    lowercase_       ,    deterministic=lowercase_					)

										if self.add_upsample:
																	lowercase_      :    Optional[Any]       		= self.upsamplers_a(lowercase_					)

										return hidden_states





class 					__magic_name__		(					nn.Module):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 0.0
			UpperCamelCase__       = 1
			UpperCamelCase__       = 1
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = jnp.floataa







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										# there is always at least one resnet
										lowercase_      :    int       		= [
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels       ,    out_channels=self.in_channels       ,    dropout_prob=self.dropout       ,    dtype=self.dtype       ,    )
										]

										lowercase_      :    str       		= []

										for _ in range(self.num_layers					):
																	lowercase_      :    Optional[int]       		= 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(lowercase_					)

																	lowercase_      :    Optional[Any]       		= FlaxResnetBlockaD(
																	    in_channels=self.in_channels       ,    out_channels=self.in_channels       ,    dropout_prob=self.dropout       ,    dtype=self.dtype       ,    )
																	resnets.append(lowercase_					)

										lowercase_      :    str       		= resnets
										lowercase_      :    int       		= attentions







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

										return hidden_states






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





