



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class      __lowerCAmelCase     (		nn.Module):
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       Any = 16				,					_snake_case					:       str = 88				,					_snake_case					:       str = None				,					_snake_case					:       Optional[int] = 1				,					_snake_case					:       Optional[Any] = 0.0				,					_snake_case					:       Optional[int] = 32				,					_snake_case					:       Optional[Any] = None				,					_snake_case					:       Union[str, Any] = False				,					_snake_case					:       Dict = None				,					_snake_case					:       Optional[Any] = None				,					_snake_case					:       Optional[int] = "geglu"				,					_snake_case					:       Dict = None				,					):






								"""simple docstring"""


								super().__init__()
								A__       =    nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=_a				,					attention_head_dim=_a				,					in_channels=_a				,					num_layers=_a				,					dropout=_a				,					norm_num_groups=_a				,					cross_attention_dim=_a				,					attention_bias=_a				,					sample_size=_a				,					num_vector_embeds=_a				,					activation_fn=_a				,					num_embeds_ada_norm=_a				,					)
								        for _ in range(2					)
								    ]					)

								# Variables that can be set by a pipeline:

								# The ratio of transformer1 to transformer2's output states to be combined during inference
								A__       =    0.5

								# The shape of `encoder_hidden_states` is expected to be
								# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
								A__       =    [77, 2_57]

								# Which transformer to use to encode which condition.
								# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
								A__       =    [1, 0]



					def     _a							(     self					:       Any				,					_snake_case					:       List[str]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       str=None				,					_snake_case					:       List[Any]=None				,					_snake_case					:       List[Any] = True				,					):






								"""simple docstring"""


								A__       =    hidden_states

								A__       =    []
								A__       =    0
								# attention_mask is not used yet
								for i in range(2					):
											# for each of the two transformers, pass the corresponding condition tokens
											A__       =    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											A__       =    self.transformer_index_for_condition[i]
											A__       =    self.transformers[transformer_index](
											    _a				,					encoder_hidden_states=_a				,					timestep=_a				,					cross_attention_kwargs=_a				,					return_dict=_a				,					)[0]
											encoded_states.append(encoded_state - input_states					)
											tokens_start += self.condition_lengths[i]

								A__       =    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
								A__       =    output_states + input_states

								if not return_dict:
											return (output_states,)

								return TransformeraDModelOutput(sample=_a					)









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




