from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a_    					=       logging.get_logger(__name__)

a_    					=       {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'marian'
 lowercase				     =   ['past_key_values']
 lowercase				     =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__( self	:							Optional[Any]   ,      snake_case	:							int=5_8_1_0_1   ,      snake_case	:							str=None   ,      snake_case	:							List[Any]=1_0_2_4   ,      snake_case	:							Optional[int]=1_2   ,      snake_case	:							Optional[Any]=4_0_9_6   ,      snake_case	:							Any=1_6   ,      snake_case	:							Any=1_2   ,      snake_case	:							Any=4_0_9_6   ,      snake_case	:							Optional[int]=1_6   ,      snake_case	:							Union[str, Any]=0.0   ,      snake_case	:							List[Any]=0.0   ,      snake_case	:							Any=True   ,      snake_case	:							Any=True   ,      snake_case	:							Any="gelu"   ,      snake_case	:							Union[str, Any]=1_0_2_4   ,      snake_case	:							Any=0.1   ,      snake_case	:							Union[str, Any]=0.0   ,      snake_case	:							int=0.0   ,      snake_case	:							Any=0.02   ,      snake_case	:							Union[str, Any]=5_8_1_0_0   ,      snake_case	:							Tuple=False   ,      snake_case	:							Optional[Any]=5_8_1_0_0   ,      snake_case	:							Dict=0   ,      snake_case	:							Union[str, Any]=0   ,      snake_case	:							Tuple=True   ,      **snake_case	:							Any   ,      )   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					vocab_size
  UpperCamelCase_					:						Optional[Any] =					decoder_vocab_size or vocab_size
  UpperCamelCase_					:						Dict =					max_position_embeddings
  UpperCamelCase_					:						List[Any] =					d_model
  UpperCamelCase_					:						Union[str, Any] =					encoder_ffn_dim
  UpperCamelCase_					:						Optional[int] =					encoder_layers
  UpperCamelCase_					:						Union[str, Any] =					encoder_attention_heads
  UpperCamelCase_					:						Tuple =					decoder_ffn_dim
  UpperCamelCase_					:						Optional[int] =					decoder_layers
  UpperCamelCase_					:						int =					decoder_attention_heads
  UpperCamelCase_					:						Optional[Any] =					dropout
  UpperCamelCase_					:						int =					attention_dropout
  UpperCamelCase_					:						Union[str, Any] =					activation_dropout
  UpperCamelCase_					:						Optional[Any] =					activation_function
  UpperCamelCase_					:						List[Any] =					init_std
  UpperCamelCase_					:						int =					encoder_layerdrop
  UpperCamelCase_					:						Optional[int] =					decoder_layerdrop
  UpperCamelCase_					:						Union[str, Any] =					use_cache
  UpperCamelCase_					:						Union[str, Any] =					encoder_layers
  UpperCamelCase_					:						Optional[Any] =					scale_embedding  # scale factor will be sqrt(d_model) if True
  UpperCamelCase_					:						Optional[int] =					share_encoder_decoder_embeddings
  super().__init__(
      pad_token_id=snake_case   ,      eos_token_id=snake_case   ,      is_encoder_decoder=snake_case   ,      decoder_start_token_id=snake_case   ,      forced_eos_token_id=snake_case   ,      **snake_case   ,      )





class 		_lowercase						(					snake_case_       ):

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Mapping[str, Mapping[int, str]]:

  """simple docstring"""





  if self.task in ["default", "seq2seq-lm"]:
   UpperCamelCase_					:						str =					OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
           ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
       ]			)

   if self.use_past:
    UpperCamelCase_					:						Any =					{0: 'batch'}
    UpperCamelCase_					:						int =					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
    UpperCamelCase_					:						Optional[Any] =					{0: 'batch', 1: 'decoder_sequence'}
    UpperCamelCase_					:						Tuple =					{0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
    self.fill_with_past_key_values_(snake_case   ,      direction='inputs'			)
  elif self.task == "causal-lm":
   # TODO: figure this case out.
   UpperCamelCase_					:						Any =					OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
           ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
       ]			)
   if self.use_past:
    UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					self.num_layers
    for i in range(snake_case			):
     UpperCamelCase_					:						Union[str, Any] =					{0: 'batch', 2: 'past_sequence + sequence'}
     UpperCamelCase_					:						str =					{0: 'batch', 2: 'past_sequence + sequence'}
  else:
   UpperCamelCase_					:						List[Any] =					OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
           ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
           ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
           ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
       ]			)

  return common_inputs

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Mapping[str, Mapping[int, str]]:

  """simple docstring"""





  if self.task in ["default", "seq2seq-lm"]:
   UpperCamelCase_					:						Tuple =					super().outputs
  else:
   UpperCamelCase_					:						int =					super(snake_case   ,      self			).outputs
   if self.use_past:
    UpperCamelCase_, UpperCamelCase_					:						Any =					self.num_layers
    for i in range(snake_case			):
     UpperCamelCase_					:						List[str] =					{0: 'batch', 2: 'past_sequence + sequence'}
     UpperCamelCase_					:						Optional[Any] =					{0: 'batch', 2: 'past_sequence + sequence'}
  return common_outputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							PreTrainedTokenizer   ,      snake_case	:							int = -1   ,      snake_case	:							int = -1   ,      snake_case	:							bool = False   ,      snake_case	:							Optional[TensorType] = None   ,      )   ->						Mapping[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					self._generate_dummy_inputs_for_encoder_and_decoder(
      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case			)

  # Generate decoder inputs
  UpperCamelCase_					:						Union[str, Any] =					seq_length if not self.use_past else 1
  UpperCamelCase_					:						Optional[int] =					self._generate_dummy_inputs_for_encoder_and_decoder(
      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case			)
  UpperCamelCase_					:						List[Any] =					{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
  UpperCamelCase_					:						Tuple =					dict(**snake_case   ,      **snake_case			)

  if self.use_past:
   if not is_torch_available():
    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
   else:
    import torch
   UpperCamelCase_, UpperCamelCase_					:						Dict =					common_inputs['input_ids'].shape
   UpperCamelCase_					:						int =					common_inputs['decoder_input_ids'].shape[1]
   UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					self.num_attention_heads
   UpperCamelCase_					:						Any =					(
       batch,
       num_encoder_attention_heads,
       encoder_seq_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )
   UpperCamelCase_					:						Union[str, Any] =					decoder_seq_length + 3
   UpperCamelCase_					:						str =					(
       batch,
       num_decoder_attention_heads,
       decoder_past_length,
       self._config.hidden_size // num_decoder_attention_heads,
   )

   UpperCamelCase_					:						Optional[int] =					torch.cat(
       [common_inputs['decoder_attention_mask'], torch.ones(snake_case   ,      snake_case			)]   ,      dim=1			)

   UpperCamelCase_					:						Any =					[]
   # If the number of encoder and decoder layers are present in the model configuration, both are considered
   UpperCamelCase_, UpperCamelCase_					:						Any =					self.num_layers
   UpperCamelCase_					:						Any =					min(snake_case   ,      snake_case			)
   UpperCamelCase_					:						Dict =					max(snake_case   ,      snake_case			) - min_num_layers
   UpperCamelCase_					:						Tuple =					'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

   for _ in range(snake_case			):
    common_inputs["past_key_values"].append(
        (
            torch.zeros(snake_case			),
            torch.zeros(snake_case			),
            torch.zeros(snake_case			),
            torch.zeros(snake_case			),
        )			)
   # TODO: test this.
   UpperCamelCase_					:						Optional[int] =					encoder_shape if remaining_side_name == 'encoder' else decoder_shape
   for _ in range(snake_case   ,      snake_case			):
    common_inputs["past_key_values"].append((torch.zeros(snake_case			), torch.zeros(snake_case			))			)
  return common_inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							PreTrainedTokenizer   ,      snake_case	:							int = -1   ,      snake_case	:							int = -1   ,      snake_case	:							bool = False   ,      snake_case	:							Optional[TensorType] = None   ,      )   ->						Mapping[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self._generate_dummy_inputs_for_encoder_and_decoder(
      snake_case   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case			)

  if self.use_past:
   if not is_torch_available():
    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
   else:
    import torch
   UpperCamelCase_, UpperCamelCase_					:						int =					common_inputs['input_ids'].shape
   # Not using the same length for past_key_values
   UpperCamelCase_					:						Dict =					seqlen + 2
   UpperCamelCase_, UpperCamelCase_					:						Any =					self.num_layers
   UpperCamelCase_, UpperCamelCase_					:						int =					self.num_attention_heads
   UpperCamelCase_					:						List[str] =					(
       batch,
       num_encoder_attention_heads,
       past_key_values_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )

   UpperCamelCase_					:						Any =					common_inputs['attention_mask'].dtype
   UpperCamelCase_					:						Optional[Any] =					torch.cat(
       [common_inputs['attention_mask'], torch.ones(snake_case   ,      snake_case   ,      dtype=snake_case			)]   ,      dim=1			)
   UpperCamelCase_					:						Tuple =					[
       (torch.zeros(snake_case			), torch.zeros(snake_case			)) for _ in range(snake_case			)
   ]
  return common_inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							PreTrainedTokenizer   ,      snake_case	:							int = -1   ,      snake_case	:							int = -1   ,      snake_case	:							bool = False   ,      snake_case	:							Optional[TensorType] = None   ,      )   ->						Mapping[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					compute_effective_axis_dimension(
      snake_case   ,      fixed_dimension=OnnxConfig.default_fixed_batch   ,      num_token_to_add=0			)

  # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  UpperCamelCase_					:						List[str] =					tokenizer.num_special_tokens_to_add(snake_case			)
  UpperCamelCase_					:						Any =					compute_effective_axis_dimension(
      snake_case   ,      fixed_dimension=OnnxConfig.default_fixed_sequence   ,      num_token_to_add=snake_case			)

  # Generate dummy inputs according to compute batch and sequence
  UpperCamelCase_					:						Dict =					[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
  UpperCamelCase_					:						List[Any] =					dict(tokenizer(snake_case   ,      return_tensors=snake_case			)			)
  return common_inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							PreTrainedTokenizer   ,      snake_case	:							int = -1   ,      snake_case	:							int = -1   ,      snake_case	:							bool = False   ,      snake_case	:							Optional[TensorType] = None   ,      )   ->						Mapping[str, Any]:

  """simple docstring"""





  if self.task in ["default", "seq2seq-lm"]:
   UpperCamelCase_					:						Any =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
       snake_case   ,      batch_size=snake_case   ,      seq_length=snake_case   ,      is_pair=snake_case   ,      framework=snake_case			)

  else:
   UpperCamelCase_					:						int =					self._generate_dummy_inputs_for_causal_lm(
       snake_case   ,      batch_size=snake_case   ,      seq_length=snake_case   ,      is_pair=snake_case   ,      framework=snake_case			)

  return common_inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Dict   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]   ,      snake_case	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  if self.task in ["default", "seq2seq-lm"]:
   UpperCamelCase_					:						Any =					super()._flatten_past_key_values_(snake_case   ,      snake_case   ,      snake_case   ,      snake_case			)
  else:
   UpperCamelCase_					:						str =					super(snake_case   ,      self			)._flatten_past_key_values_(
       snake_case   ,      snake_case   ,      snake_case   ,      snake_case			)


 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						float:

  """simple docstring"""





  return 1e-4




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



