

"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   _a				   =							'platform'

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def 						_A						(						UpperCamelCase_  : Any,    UpperCamelCase_  : Tuple,    UpperCamelCase_  : Dict=None,    UpperCamelCase_  : Optional[Any]=None)			->		int:
   '''simple docstring'''


   if attention_mask is None:
      __lowercase			=		np.where(input_ids != config.pad_token_id,    1,    0)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }




@require_flax
class       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    List[Any],      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    int=1_3,      UpperCAmelCase__							:    List[str]=7,      UpperCAmelCase__							:    List[str]=True,      UpperCAmelCase__							:    Dict=False,      UpperCAmelCase__							:    str=9_9,      UpperCAmelCase__							:    List[Any]=1_6,      UpperCAmelCase__							:    Optional[int]=2,      UpperCAmelCase__							:    Any=4,      UpperCAmelCase__							:    Optional[int]=4,      UpperCAmelCase__							:    List[str]="gelu",      UpperCAmelCase__							:    str=0.1,      UpperCAmelCase__							:    int=0.1,      UpperCAmelCase__							:    int=2_0,      UpperCAmelCase__							:    Any=2,      UpperCAmelCase__							:    Union[str, Any]=1,      UpperCAmelCase__							:    Optional[Any]=0,      UpperCAmelCase__							:    Any=1_6,      UpperCAmelCase__							:    Any=1_6,      UpperCAmelCase__							:    Any=0.02,      ):
    __lowercase			=		parent
    __lowercase			=		batch_size
    __lowercase			=		seq_length
    __lowercase			=		is_training
    __lowercase			=		use_labels
    __lowercase			=		vocab_size
    __lowercase			=		hidden_size
    __lowercase			=		num_hidden_layers
    __lowercase			=		num_attention_heads
    __lowercase			=		intermediate_size
    __lowercase			=		hidden_act
    __lowercase			=		hidden_dropout_prob
    __lowercase			=		attention_probs_dropout_prob
    __lowercase			=		max_position_embeddings
    __lowercase			=		eos_token_id
    __lowercase			=		pad_token_id
    __lowercase			=		bos_token_id
    __lowercase			=		embed_dim
    __lowercase			=		word_embed_proj_dim
    __lowercase			=		initializer_range
    __lowercase			=		False


 def       _lowercase				(    self							:    str			):
    __lowercase			=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1],      self.vocab_size			),      3,      self.vocab_size			)
    __lowercase			=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),      dtype=np.intaa			)),      -1			)

    __lowercase			=		OPTConfig(
        vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      ffn_dim=self.intermediate_size,      dropout=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      eos_token_id=self.eos_token_id,      bos_token_id=self.bos_token_id,      pad_token_id=self.pad_token_id,      embed_dim=self.embed_dim,      is_encoder_decoder=UpperCAmelCase__,      word_embed_proj_dim=self.word_embed_proj_dim,      initializer_range=self.initializer_range,      use_cache=UpperCAmelCase__,      )
    __lowercase			=		prepare_opt_inputs_dict(UpperCAmelCase__,      UpperCAmelCase__			)
    return config, inputs_dict


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase	,__lowercase			=		self.prepare_config_and_inputs()
    return config, inputs_dict


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]			):
    __lowercase			=		2_0
    __lowercase			=		model_class_name(UpperCAmelCase__			)

    __lowercase			=		inputs_dict["input_ids"]
    __lowercase			=		inputs_dict["attention_mask"]

    __lowercase			=		model.init_cache(input_ids.shape[0],      UpperCAmelCase__			)
    __lowercase			=		jnp.ones((input_ids.shape[0], max_length),      dtype="i4"			)

    __lowercase			=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1			)[None, :],      (input_ids.shape[0], input_ids.shape[-1] - 1),      )
    __lowercase			=		model(
        input_ids[:, :-1],      attention_mask=UpperCAmelCase__,      past_key_values=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )

    __lowercase			=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],      dtype="i4"			)
    __lowercase			=		model(
        input_ids[:, -1:],      attention_mask=UpperCAmelCase__,      past_key_values=outputs_cache.past_key_values,      position_ids=UpperCAmelCase__,      )

    __lowercase			=		model(UpperCAmelCase__			)

    __lowercase			=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
    self.parent.assertTrue(diff < 1E-3,      msg=F"""Max diff is {diff}"""			)



 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Any			):
    __lowercase			=		2_0
    __lowercase			=		model_class_name(UpperCAmelCase__			)

    __lowercase	,__lowercase			=		(
        inputs_dict["input_ids"],
        inputs_dict["attention_mask"],
    )

    __lowercase			=		jnp.concatenate(
        [
            attention_mask,
            jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])			),
        ],      axis=-1,      )

    __lowercase			=		model.init_cache(input_ids.shape[0],      UpperCAmelCase__			)
    __lowercase			=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1			)[None, :],      (input_ids.shape[0], input_ids.shape[-1] - 1),      )

    __lowercase			=		model(
        input_ids[:, :-1],      attention_mask=UpperCAmelCase__,      past_key_values=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )
    __lowercase			=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],      dtype="i4"			)
    __lowercase			=		model(
        input_ids[:, -1:],      past_key_values=outputs_cache.past_key_values,      attention_mask=UpperCAmelCase__,      position_ids=UpperCAmelCase__,      )

    __lowercase			=		model(UpperCAmelCase__,      attention_mask=UpperCAmelCase__			)

    __lowercase			=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
    self.parent.assertTrue(diff < 1E-3,      msg=F"""Max diff is {diff}"""			)







@require_flax
class       _lowerCAmelCase						(			lowercase_  ,unittest.TestCase  ,lowercase_     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[int]     	=			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 __UpperCAmelCase			:      Optional[int]     	=			() if is_flax_available() else ()


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		FlaxOPTModelTester(self			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)



 @slow
 def       _lowercase				(    self							:    List[str]			):







    for model_class_name in self.all_model_classes:
       __lowercase			=		model_class_name.from_pretrained("facebook/opt-125m"			)
       __lowercase			=		np.ones((1, 1)			) * model.config.eos_token_id
       __lowercase			=		model(UpperCAmelCase__			)
       self.assertIsNotNone(UpperCAmelCase__			)







@require_sentencepiece
@require_flax
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @slow
 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		FlaxOPTModel.from_pretrained("facebook/opt-350m"			)
    __lowercase			=		jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
    __lowercase			=		model(input_ids=UpperCAmelCase__			).last_hidden_state
    __lowercase			=		(1, 1_1, 5_1_2)
    self.assertEqual(output.shape,      UpperCAmelCase__			)
    __lowercase			=		jnp.array(
        [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]			)
    self.assertTrue(jnp.allclose(output[:, :3, :3],      UpperCAmelCase__,      atol=4E-2			)			)







@require_flax
@slow
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Any			):
    super().setUp()
    __lowercase			=		"facebook/opt-350m"



 def       _lowercase				(    self							:    str			):
    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(self.path_model			)
    __lowercase			=		GPTaTokenizer.from_pretrained(self.path_model			)

    __lowercase			=		[
        "Today is a beautiful day and I want to",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]
    # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
    __lowercase			=		tokenizer(UpperCAmelCase__,      return_tensors="jax",      padding=UpperCAmelCase__,      add_special_tokens=UpperCAmelCase__			)
    __lowercase			=		model(inputs.input_ids,      attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
    __lowercase			=		jnp.array(
        [
            [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
            [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
            [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
            [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
        ]			)
    self.assertTrue(jnp.allclose(UpperCAmelCase__,      UpperCAmelCase__,      atol=4E-2			)			)

    __lowercase			=		jax.jit(UpperCAmelCase__			)
    __lowercase			=		model(inputs.input_ids,      attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
    self.assertTrue(jnp.allclose(UpperCAmelCase__,      UpperCAmelCase__,      atol=4E-2			)			)







@require_flax
@slow
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @property
 def       _lowercase				(    self							:    Optional[int]			):
    return [
        "Today is a beautiful day and I want",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		"facebook/opt-125m"

    __lowercase			=		[
        "Today is a beautiful day and I want to",
        "In the city of New York, the city",
        "Paris is the capital of France and the capital",
        "Computers and mobile phones have taken over the",
    ]

    __lowercase			=		[]

    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)

    for prompt in self.prompts:
       __lowercase			=		tokenizer(UpperCAmelCase__,      return_tensors="jax"			).input_ids

       __lowercase			=		model.generate(UpperCAmelCase__,      max_length=1_0			)
       __lowercase			=		generated_ids[0]

       __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)
       predicted_outputs += generated_string

    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		"facebook/opt-350m"

    __lowercase			=		[
        "Today is a beautiful day and I want to",
        "In the city of San Francisco, the city",
        "Paris is the capital of France and the capital",
        "Computers and mobile phones have taken over the",
    ]

    __lowercase			=		[]
    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)

    for prompt in self.prompts:
       __lowercase			=		tokenizer(UpperCAmelCase__,      return_tensors="jax"			).input_ids

       __lowercase			=		model.generate(UpperCAmelCase__,      max_length=1_0			)
       __lowercase			=		generated_ids[0]

       __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)
       predicted_outputs += generated_string

    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		"facebook/opt-125m"
    __lowercase			=		[
        "Today is a beautiful day and I want to thank",
        "In the city of Rome Canaver Canaver Canaver Canaver",
    ]
    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		tokenizer(
        [
            "Today is a beautiful day and I want to",
            "In the city of",
        ],      return_tensors="jax",      padding=UpperCAmelCase__,      )

    __lowercase			=		jax.jit(model.generate			)

    __lowercase			=		jit_generate(inputs["input_ids"],      attention_mask=inputs["attention_mask"]			).sequences

    __lowercase			=		tokenizer.batch_decode(UpperCAmelCase__,      skip_special_tokens=UpperCAmelCase__			)

    self.assertIsNotNone(UpperCAmelCase__,      UpperCAmelCase__			)



 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		"facebook/opt-350m"

    __lowercase			=		GPTaTokenizer.from_pretrained(UpperCAmelCase__			)
    __lowercase			=		FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase__			)

    __lowercase			=		"left"

    # use different length sentences to test batching
    __lowercase			=		[
        "Hello, my dog is a little",
        "Today, I",
    ]

    __lowercase			=		tokenizer(UpperCAmelCase__,      return_tensors="jax",      padding=UpperCAmelCase__			)
    __lowercase			=		inputs["input_ids"]

    __lowercase			=		model.generate(input_ids=UpperCAmelCase__,      attention_mask=inputs["attention_mask"],      trace=UpperCAmelCase__			)

    __lowercase			=		tokenizer(sentences[0],      return_tensors="jax"			).input_ids
    __lowercase			=		model.generate(input_ids=UpperCAmelCase__			)

    __lowercase			=		inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
    __lowercase			=		tokenizer(sentences[1],      return_tensors="jax"			).input_ids
    __lowercase			=		model.generate(input_ids=UpperCAmelCase__,      max_length=model.config.max_length - num_paddings			)

    __lowercase			=		tokenizer.batch_decode(outputs[0],      skip_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.decode(output_non_padded[0][0],      skip_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.decode(output_padded[0][0],      skip_special_tokens=UpperCAmelCase__			)

    __lowercase			=		[
        "Hello, my dog is a little bit of a dork.\nI\'m a little bit",
        "Today, I was in the middle of a conversation with a friend about the",
    ]
    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertListEqual(UpperCAmelCase__,      [non_padded_sentence, padded_sentence]			)



"""simple docstring"""





import functools
import math

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




def 						_A						(						UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : List[Any],    UpperCamelCase_  : Dict,    UpperCamelCase_  : int = 4096)			->		Optional[Any]:
   '''simple docstring'''


   __lowercase	,__lowercase	,__lowercase			=		key.shape[-3:]
   __lowercase			=		value.shape[-1]
   __lowercase			=		min(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		query / jnp.sqrt(UpperCamelCase_)

   @functools.partial(jax.checkpoint,    prevent_cse=UpperCamelCase_)
   def summarize_chunk(UpperCamelCase_  : int,    UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Any):
      __lowercase			=		jnp.einsum("...qhd,...khd->...qhk",    UpperCamelCase_,    UpperCamelCase_,    precision=UpperCamelCase_)

      __lowercase			=		jnp.max(UpperCamelCase_,    axis=-1,    keepdims=UpperCamelCase_)
      __lowercase			=		jax.lax.stop_gradient(UpperCamelCase_)
      __lowercase			=		jnp.exp(attn_weights - max_score)

      __lowercase			=		jnp.einsum("...vhf,...qhv->...qhf",    UpperCamelCase_,    UpperCamelCase_,    precision=UpperCamelCase_)
      __lowercase			=		jnp.einsum("...qhk->...qh",    UpperCamelCase_)

      return (exp_values, exp_weights.sum(axis=-1), max_score)

   def chunk_scanner(UpperCamelCase_  : int):
      # julienne key array
      __lowercase			=		jax.lax.dynamic_slice(
          operand=UpperCamelCase_,    start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0],    slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features],    )

      # julienne value array
      __lowercase			=		jax.lax.dynamic_slice(
          operand=UpperCamelCase_,    start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0],    slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features],    )

      return summarize_chunk(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)

   __lowercase	,__lowercase	,__lowercase			=		jax.lax.map(f=UpperCamelCase_,    xs=jnp.arange(0,    UpperCamelCase_,    UpperCamelCase_))

   __lowercase			=		jnp.max(UpperCamelCase_,    axis=0,    keepdims=UpperCamelCase_)
   __lowercase			=		jnp.exp(chunk_max - global_max)

   chunk_values *= jnp.expand_dims(UpperCamelCase_,    axis=-1)
   chunk_weights *= max_diffs

   __lowercase			=		chunk_values.sum(axis=0)
   __lowercase			=		jnp.expand_dims(UpperCamelCase_,    -1).sum(axis=0)

   return all_values / all_weights




def 						_A						(						UpperCamelCase_  : Optional[Any],    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : List[str],    UpperCamelCase_  : Union[str, Any]=jax.lax.Precision.HIGHEST,    UpperCamelCase_  : int = 1024,    UpperCamelCase_  : int = 4096)			->		str:
   '''simple docstring'''


   __lowercase	,__lowercase	,__lowercase			=		query.shape[-3:]

   def chunk_scanner(UpperCamelCase_  : List[str],    UpperCamelCase_  : str):
      # julienne query array
      __lowercase			=		jax.lax.dynamic_slice(
          operand=UpperCamelCase_,    start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0],    slice_sizes=list(query.shape[:-3]) + [min(UpperCamelCase_,    UpperCamelCase_), num_heads, q_features],    )

      return (
          chunk_idx + query_chunk_size,  # unused ignore it
          _query_chunk_attention(
              query=UpperCamelCase_,    key=UpperCamelCase_,    value=UpperCamelCase_,    precision=UpperCamelCase_,    key_chunk_size=UpperCamelCase_),
      )

   __lowercase	,__lowercase			=		jax.lax.scan(
       f=UpperCamelCase_,    init=0,    xs=UpperCamelCase_,    length=math.ceil(num_q / query_chunk_size))

   return jnp.concatenate(UpperCamelCase_,    axis=-3)  # fuse the chunked result back




class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int     	=			8
 __UpperCAmelCase			:      int     	=			6_4
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		self.dim_head * self.heads
    __lowercase			=		self.dim_head**-0.5

    # Weights were exported with old names {to_q, to_k, to_v, to_out}
    __lowercase			=		nn.Dense(UpperCAmelCase__,      use_bias=UpperCAmelCase__,      dtype=self.dtype,      name="to_q"			)
    __lowercase			=		nn.Dense(UpperCAmelCase__,      use_bias=UpperCAmelCase__,      dtype=self.dtype,      name="to_k"			)
    __lowercase			=		nn.Dense(UpperCAmelCase__,      use_bias=UpperCAmelCase__,      dtype=self.dtype,      name="to_v"			)

    __lowercase			=		nn.Dense(self.query_dim,      dtype=self.dtype,      name="to_out_0"			)
    __lowercase			=		nn.Dropout(rate=self.dropout			)


 def       _lowercase				(    self							:    Any,      UpperCAmelCase__							:    Union[str, Any]			):
    __lowercase	,__lowercase	,__lowercase			=		tensor.shape
    __lowercase			=		self.heads
    __lowercase			=		tensor.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      dim // head_size			)
    __lowercase			=		jnp.transpose(UpperCAmelCase__,      (0, 2, 1, 3)			)
    __lowercase			=		tensor.reshape(batch_size * head_size,      UpperCAmelCase__,      dim // head_size			)
    return tensor


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    int			):
    __lowercase	,__lowercase	,__lowercase			=		tensor.shape
    __lowercase			=		self.heads
    __lowercase			=		tensor.reshape(batch_size // head_size,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
    __lowercase			=		jnp.transpose(UpperCAmelCase__,      (0, 2, 1, 3)			)
    __lowercase			=		tensor.reshape(batch_size // head_size,      UpperCAmelCase__,      dim * head_size			)
    return tensor



 def __call__(    self							:    List[Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Union[str, Any]=None,      UpperCAmelCase__							:    Optional[Any]=True			):
    __lowercase			=		hidden_states if context is None else context

    __lowercase			=		self.query(UpperCAmelCase__			)
    __lowercase			=		self.key(UpperCAmelCase__			)
    __lowercase			=		self.value(UpperCAmelCase__			)

    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)
    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)
    __lowercase			=		self.reshape_heads_to_batch_dim(UpperCAmelCase__			)

    if self.use_memory_efficient_attention:
       __lowercase			=		query_states.transpose(1,      0,      2			)
       __lowercase			=		key_states.transpose(1,      0,      2			)
       __lowercase			=		value_states.transpose(1,      0,      2			)

       # this if statement create a chunk size for each layer of the unet
       # the chunk size is equal to the query_length dimension of the deepest layer of the unet

       __lowercase			=		query_states.shape[-3]
       if flatten_latent_dim % 6_4 == 0:
          __lowercase			=		int(flatten_latent_dim / 6_4			)
       elif flatten_latent_dim % 1_6 == 0:
          __lowercase			=		int(flatten_latent_dim / 1_6			)
       elif flatten_latent_dim % 4 == 0:
          __lowercase			=		int(flatten_latent_dim / 4			)
       else:
          __lowercase			=		int(UpperCAmelCase__			)

       __lowercase			=		jax_memory_efficient_attention(
           UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      query_chunk_size=UpperCAmelCase__,      key_chunk_size=4_0_9_6 * 4			)

       __lowercase			=		hidden_states.transpose(1,      0,      2			)
    else:
       # compute attentions
       __lowercase			=		jnp.einsum("b i d, b j d->b i j",      UpperCAmelCase__,      UpperCAmelCase__			)
       __lowercase			=		attention_scores * self.scale
       __lowercase			=		nn.softmax(UpperCAmelCase__,      axis=2			)

       # attend to values
       __lowercase			=		jnp.einsum("b i j, b j d -> b i d",      UpperCAmelCase__,      UpperCAmelCase__			)

    __lowercase			=		self.reshape_batch_dim_to_heads(UpperCAmelCase__			)
    __lowercase			=		self.proj_attn(UpperCAmelCase__			)
    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa
 __UpperCAmelCase			:      bool     	=			False


 def       _lowercase				(    self							:    Tuple			):
    # self attention (or cross_attention if only_cross_attention is True)
    __lowercase			=		FlaxAttention(
        self.dim,      self.n_heads,      self.d_head,      self.dropout,      self.use_memory_efficient_attention,      dtype=self.dtype			)
    # cross attention
    __lowercase			=		FlaxAttention(
        self.dim,      self.n_heads,      self.d_head,      self.dropout,      self.use_memory_efficient_attention,      dtype=self.dtype			)
    __lowercase			=		FlaxFeedForward(dim=self.dim,      dropout=self.dropout,      dtype=self.dtype			)
    __lowercase			=		nn.LayerNorm(epsilon=1E-5,      dtype=self.dtype			)
    __lowercase			=		nn.LayerNorm(epsilon=1E-5,      dtype=self.dtype			)
    __lowercase			=		nn.LayerNorm(epsilon=1E-5,      dtype=self.dtype			)
    __lowercase			=		nn.Dropout(rate=self.dropout			)



 def __call__(    self							:    str,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    Dict=True			):
    # self attention
    __lowercase			=		hidden_states
    if self.only_cross_attention:
       __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    else:
       __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    # cross attention
    __lowercase			=		hidden_states
    __lowercase			=		self.attna(self.norma(UpperCAmelCase__			),      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    # feed forward
    __lowercase			=		hidden_states
    __lowercase			=		self.ff(self.norma(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)
    __lowercase			=		hidden_states + residual

    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      int     	=			1
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      bool     	=			False
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa
 __UpperCAmelCase			:      bool     	=			False


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		nn.GroupNorm(num_groups=3_2,      epsilon=1E-5			)

    __lowercase			=		self.n_heads * self.d_head
    if self.use_linear_projection:
       __lowercase			=		nn.Dense(UpperCAmelCase__,      dtype=self.dtype			)
    else:
       __lowercase			=		nn.Conv(
           UpperCAmelCase__,      kernel_size=(1, 1),      strides=(1, 1),      padding="VALID",      dtype=self.dtype,      )

    __lowercase			=		[
        FlaxBasicTransformerBlock(
            UpperCAmelCase__,      self.n_heads,      self.d_head,      dropout=self.dropout,      only_cross_attention=self.only_cross_attention,      dtype=self.dtype,      use_memory_efficient_attention=self.use_memory_efficient_attention,      )
        for _ in range(self.depth			)
    ]

    if self.use_linear_projection:
       __lowercase			=		nn.Dense(UpperCAmelCase__,      dtype=self.dtype			)
    else:
       __lowercase			=		nn.Conv(
           UpperCAmelCase__,      kernel_size=(1, 1),      strides=(1, 1),      padding="VALID",      dtype=self.dtype,      )

    __lowercase			=		nn.Dropout(rate=self.dropout			)



 def __call__(    self							:    Dict,      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    List[str]=True			):
    __lowercase	,__lowercase	,__lowercase	,__lowercase			=		hidden_states.shape
    __lowercase			=		hidden_states
    __lowercase			=		self.norm(UpperCAmelCase__			)
    if self.use_linear_projection:
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      height * width,      UpperCAmelCase__			)
       __lowercase			=		self.proj_in(UpperCAmelCase__			)
    else:
       __lowercase			=		self.proj_in(UpperCAmelCase__			)
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      height * width,      UpperCAmelCase__			)

    for transformer_block in self.transformer_blocks:
       __lowercase			=		transformer_block(UpperCAmelCase__,      UpperCAmelCase__,      deterministic=UpperCAmelCase__			)

    if self.use_linear_projection:
       __lowercase			=		self.proj_out(UpperCAmelCase__			)
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
    else:
       __lowercase			=		hidden_states.reshape(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
       __lowercase			=		self.proj_out(UpperCAmelCase__			)

    __lowercase			=		hidden_states + residual
    return self.dropout_layer(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    List[str]			):
    # The second linear layer needs to be called
    # net_2 for now to match the index of the Sequential layer
    __lowercase			=		FlaxGEGLU(self.dim,      self.dropout,      self.dtype			)
    __lowercase			=		nn.Dense(self.dim,      dtype=self.dtype			)



 def __call__(    self							:    int,      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Union[str, Any]=True			):
    __lowercase			=		self.net_a(UpperCAmelCase__,      deterministic=UpperCAmelCase__			)
    __lowercase			=		self.net_a(UpperCAmelCase__			)
    return hidden_states







class       _lowerCAmelCase						(			nn.Module     ):







 """simple docstring"""







 __UpperCAmelCase			:      int
 __UpperCAmelCase			:      float     	=			0.0
 __UpperCAmelCase			:      jnp.dtype     	=			jnp.floataa


 def       _lowercase				(    self							:    int			):
    __lowercase			=		self.dim * 4
    __lowercase			=		nn.Dense(inner_dim * 2,      dtype=self.dtype			)
    __lowercase			=		nn.Dropout(rate=self.dropout			)



 def __call__(    self							:    Optional[Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    int=True			):
    __lowercase			=		self.proj(UpperCAmelCase__			)
    __lowercase	,__lowercase			=		jnp.split(UpperCAmelCase__,      2,      axis=2			)
    return self.dropout_layer(hidden_linear * nn.gelu(UpperCAmelCase__			),      deterministic=UpperCAmelCase__			)




