






"""simple docstring"""



import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  str    ):





  '''simple docstring'''





  def get_masked_lm_array(__snake_case			:  str    ):
    lowercase							    =       f'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase							    =       tf.train.load_variable(__snake_case		,					__snake_case    )

    if "kernel" in name:
      lowercase							    =       array.transpose()

    return torch.from_numpy(__snake_case    )

  def get_encoder_array(__snake_case			:  str    ):
    lowercase							    =       f'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase							    =       tf.train.load_variable(__snake_case		,					__snake_case    )

    if "kernel" in name:
      lowercase							    =       array.transpose()

    return torch.from_numpy(__snake_case    )

  def get_encoder_layer_array(__snake_case			:  int		,					__snake_case			:  str    ):
    lowercase							    =       f'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase							    =       tf.train.load_variable(__snake_case		,					__snake_case    )

    if "kernel" in name:
      lowercase							    =       array.transpose()

    return torch.from_numpy(__snake_case    )

  def get_encoder_attention_layer_array(__snake_case			:  int		,					__snake_case			:  str		,					__snake_case			:  Optional[Any]    ):
    lowercase							    =       f'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
    lowercase							    =       tf.train.load_variable(__snake_case		,					__snake_case    )
    lowercase							    =       array.reshape(__snake_case    )

    if "kernel" in name:
      lowercase							    =       array.transpose()

    return torch.from_numpy(__snake_case    )

  print(f'Loading model based on config from {config_path}...'    )
  lowercase							    =       BertConfig.from_json_file(__snake_case    )
  lowercase							    =       BertForMaskedLM(__snake_case    )

  # Layers
  for layer_index in range(0		,					config.num_hidden_layers    ):
    lowercase							    =       model.bert.encoder.layer[layer_index]

    # Self-attention
    lowercase							    =       layer.attention.self

    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_query_dense/kernel'		,					self_attn.query.weight.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_query_dense/bias'		,					self_attn.query.bias.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_key_dense/kernel'		,					self_attn.key.weight.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_key_dense/bias'		,					self_attn.key.bias.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_value_dense/kernel'		,					self_attn.value.weight.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_value_dense/bias'		,					self_attn.value.bias.data.shape    )

    # Self-attention Output
    lowercase							    =       layer.attention.output

    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_output_dense/kernel'		,					self_output.dense.weight.data.shape    )
    lowercase							    =       get_encoder_attention_layer_array(
        __snake_case		,					'_output_dense/bias'		,					self_output.dense.bias.data.shape    )

    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_attention_layer_norm/gamma'    )
    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_attention_layer_norm/beta'    )

    # Intermediate
    lowercase							    =       layer.intermediate

    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_intermediate_dense/kernel'    )
    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_intermediate_dense/bias'    )

    # Output
    lowercase							    =       layer.output

    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_output_dense/kernel'    )
    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_output_dense/bias'    )

    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_output_layer_norm/gamma'    )
    lowercase							    =       get_encoder_layer_array(__snake_case		,					'_output_layer_norm/beta'    )

  # Embeddings
  lowercase							    =       get_encoder_array('_position_embedding_layer/embeddings'    )
  lowercase							    =       get_encoder_array('_type_embedding_layer/embeddings'    )
  lowercase							    =       get_encoder_array('_embedding_norm_layer/gamma'    )
  lowercase							    =       get_encoder_array('_embedding_norm_layer/beta'    )

  # LM Head
  lowercase							    =       model.cls.predictions.transform

  lowercase							    =       get_masked_lm_array('dense/kernel'    )
  lowercase							    =       get_masked_lm_array('dense/bias'    )

  lowercase							    =       get_masked_lm_array('layer_norm/gamma'    )
  lowercase							    =       get_masked_lm_array('layer_norm/beta'    )

  lowercase							    =       get_masked_lm_array('embedding_table'    )

  # Pooling
  lowercase							    =       BertPooler(config=__snake_case    )
  lowercase							    =       get_encoder_array('_pooler_layer/kernel'    )
  lowercase							    =       get_encoder_array('_pooler_layer/bias'    )

  # Export final model
  model.save_pretrained(__snake_case    )

  # Integration test - should load without any errors ;)
  lowercase							    =       BertForMaskedLM.from_pretrained(__snake_case    )
  print(new_model.eval()    )

  print('Model conversion was done sucessfully!'    )


if __name__ == "__main__":
    _UpperCamelCase		:  str						=		argparse.ArgumentParser()
    parser.add_argument(
        '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
    )
    parser.add_argument(
        '--bert_config_file',
        type=str,
        required=True,
        help='The config json file corresponding to the BERT model. This specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path',
        type=str,
        required=True,
        help='Path to the output PyTorch model.',
    )
    _UpperCamelCase		:  Optional[int]						=		parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










"""simple docstring"""



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        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







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

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

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

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'stabilityai/stable-diffusion-2'
      lowercase    ,     lowercase							    =       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase							,		subfolder='scheduler'       )
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          _lowerCamelCase							,		scheduler=_lowerCamelCase							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)
      lowercase							    =       scheduler_params

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

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

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


