



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      ):
				'''simple docstring'''







				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


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

									UpperCamelCase_		  =   "A painting of a squirrel eating a burger"
									UpperCamelCase_		  =   jax.device_count()
									UpperCamelCase_		  =   num_samples * [prompt]
									UpperCamelCase_		  =   sd_pipe.prepare_inputs(snake_case__    )

									UpperCamelCase_		  =   replicate(snake_case__    )
									UpperCamelCase_		  =   shard(snake_case__    )

									UpperCamelCase_		  =   jax.random.PRNGKey(0    )
									UpperCamelCase_		  =   jax.random.split(snake_case__     , jax.device_count()    )

									UpperCamelCase_		  =   sd_pipe(snake_case__     , snake_case__     , snake_case__     , num_inference_steps=25     , jit=snake_case__    )[0]
									assert images.shape == (jax.device_count(), 1, 768, 768, 3)

									UpperCamelCase_		  =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
									UpperCamelCase_		  =   images[0, 253:256, 253:256, -1]

									UpperCamelCase_		  =   jnp.asarray(jax.device_get(image_slice.flatten()    )    )
									UpperCamelCase_		  =   jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]    )
									print(F"""output_slice: {output_slice}"""    )
									assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   "stabilityai/stable-diffusion-2"
									UpperCamelCase_  ,    UpperCamelCase_		  =   FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case__     , subfolder="scheduler"    )
									UpperCamelCase_  ,    UpperCamelCase_		  =   FlaxStableDiffusionPipeline.from_pretrained(
									    snake_case__     , scheduler=snake_case__     , revision="bf16"     , dtype=jnp.bfloataa     , )
									UpperCamelCase_		  =   scheduler_params

									UpperCamelCase_		  =   "A painting of a squirrel eating a burger"
									UpperCamelCase_		  =   jax.device_count()
									UpperCamelCase_		  =   num_samples * [prompt]
									UpperCamelCase_		  =   sd_pipe.prepare_inputs(snake_case__    )

									UpperCamelCase_		  =   replicate(snake_case__    )
									UpperCamelCase_		  =   shard(snake_case__    )

									UpperCamelCase_		  =   jax.random.PRNGKey(0    )
									UpperCamelCase_		  =   jax.random.split(snake_case__     , jax.device_count()    )

									UpperCamelCase_		  =   sd_pipe(snake_case__     , snake_case__     , snake_case__     , num_inference_steps=25     , jit=snake_case__    )[0]
									assert images.shape == (jax.device_count(), 1, 768, 768, 3)

									UpperCamelCase_		  =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
									UpperCamelCase_		  =   images[0, 253:256, 253:256, -1]

									UpperCamelCase_		  =   jnp.asarray(jax.device_get(image_slice.flatten()    )    )
									UpperCamelCase_		  =   jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]    )
									print(F"""output_slice: {output_slice}"""    )
									assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def    _lowerCAmelCase    (_lowerCAmelCase):
					return ConvertCommand(
					    args.model_type   ,	args.tf_checkpoint   ,	args.pytorch_dump_output   ,	args.config   ,	args.finetuning_task_name)


UpperCAmelCase   :							Dict							 ="""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""






class       _lowercase   (a_      ):
				'''simple docstring'''







				@staticmethod
				def       _lowerCamelCase					( snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   parser.add_parser(
									    "convert"     , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."     , )
									train_parser.add_argument("--model_type"     , type=snake_case__     , required=snake_case__     , help="Model's type."    )
									train_parser.add_argument(
									    "--tf_checkpoint"     , type=snake_case__     , required=snake_case__     , help="TensorFlow checkpoint path or folder."    )
									train_parser.add_argument(
									    "--pytorch_dump_output"     , type=snake_case__     , required=snake_case__     , help="Path to the PyTorch saved model output."    )
									train_parser.add_argument("--config"     , type=snake_case__     , default=""     , help="Configuration file path or folder."    )
									train_parser.add_argument(
									    "--finetuning_task_name"     , type=snake_case__     , default=snake_case__     , help="Optional fine-tuning task name if the TF model was a finetuned model."     , )
									train_parser.set_defaults(func=snake_case__    )

				def __init__( self     , snake_case__     , snake_case__     , snake_case__     , snake_case__     , snake_case__     , *snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   logging.get_logger("transformers-cli/converting"    )

									self._logger.info(F"""Loading model {model_type}"""    )
									UpperCamelCase_		  =   model_type
									UpperCamelCase_		  =   tf_checkpoint
									UpperCamelCase_		  =   pytorch_dump_output
									UpperCamelCase_		  =   config
									UpperCamelCase_		  =   finetuning_task_name

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									if self._model_type == "albert":
														try:
																			from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																			    convert_tf_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "bert":
														try:
																			from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																			    convert_tf_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "funnel":
														try:
																			from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																			    convert_tf_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "t5":
														try:
																			from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "gpt":
														from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
														    convert_openai_checkpoint_to_pytorch,
														)

														convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "transfo_xl":
														try:
																			from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																			    convert_transfo_xl_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														if "ckpt" in self._tf_checkpoint.lower():
																			UpperCamelCase_		  =   self._tf_checkpoint
																			UpperCamelCase_		  =   ""
														else:
																			UpperCamelCase_		  =   self._tf_checkpoint
																			UpperCamelCase_		  =   ""
														convert_transfo_xl_checkpoint_to_pytorch(
														    snake_case__     , self._config     , self._pytorch_dump_output     , snake_case__    )
									elif self._model_type == "gpt2":
														try:
																			from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																			    convert_gpta_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									elif self._model_type == "xlnet":
														try:
																			from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																			    convert_xlnet_checkpoint_to_pytorch,
																			)
														except ImportError:
																			raise ImportError(snake_case__    )

														convert_xlnet_checkpoint_to_pytorch(
														    self._tf_checkpoint     , self._config     , self._pytorch_dump_output     , self._finetuning_task_name    )
									elif self._model_type == "xlm":
														from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
														    convert_xlm_checkpoint_to_pytorch,
														)

														convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     , self._pytorch_dump_output    )
									elif self._model_type == "lxmert":
														from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
														    convert_lxmert_checkpoint_to_pytorch,
														)

														convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     , self._pytorch_dump_output    )
									elif self._model_type == "rembert":
														from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
														    convert_rembert_tf_checkpoint_to_pytorch,
														)

														convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     , self._config     , self._pytorch_dump_output    )
									else:
														raise ValueError(
														    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"    )
