


"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 		lowercase							(     _snake_case						:      Dict     )     ->Union[str, Any]:
	"""simple docstring"""





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


SCREAMING_SNAKE_CASE :    List[Any]									=				"""
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 			_UpperCAmelCase (							__A				):






		'''simple docstring'''

		@staticmethod
		def 				SCREAMING_SNAKE_CASE  (a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				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=_UpperCamelCase				,       required=_UpperCamelCase				,       help='''Model\'s type.'''					)
			train_parser.add_argument(
			    '''--tf_checkpoint'''				,       type=_UpperCamelCase				,       required=_UpperCamelCase				,       help='''TensorFlow checkpoint path or folder.'''					)
			train_parser.add_argument(
			    '''--pytorch_dump_output'''				,       type=_UpperCamelCase				,       required=_UpperCamelCase				,       help='''Path to the PyTorch saved model output.'''					)
			train_parser.add_argument('''--config'''				,       type=_UpperCamelCase				,       default=''''''				,       help='''Configuration file path or folder.'''					)
			train_parser.add_argument(
			    '''--finetuning_task_name'''				,       type=_UpperCamelCase				,       default=_UpperCamelCase				,       help='''Optional fine-tuning task name if the TF model was a finetuned model.'''				,       )
			train_parser.set_defaults(func=_UpperCamelCase					)



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





			'''simple docstring'''

			__snake_case	:						Dict						=				logging.get_logger('''transformers-cli/converting'''					)

			self._logger.info(f"""Loading model {model_type}"""					)
			__snake_case	:						Dict						=				model_type
			__snake_case	:						Union[str, Any]						=				tf_checkpoint
			__snake_case	:						Optional[int]						=				pytorch_dump_output
			__snake_case	:						Dict						=				config
			__snake_case	:						List[str]						=				finetuning_task_name



		def 				SCREAMING_SNAKE_CASE  (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(_UpperCamelCase					)

				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(_UpperCamelCase					)

				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(_UpperCamelCase					)

				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(_UpperCamelCase					)

				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(_UpperCamelCase					)

				if "ckpt" in self._tf_checkpoint.lower():
					__snake_case	:						str						=				self._tf_checkpoint
					__snake_case	:						Any						=				''''''
				else:
					__snake_case	:						int						=				self._tf_checkpoint
					__snake_case	:						Optional[Any]						=				''''''
				convert_transfo_xl_checkpoint_to_pytorch(
				    _UpperCamelCase				,       self._config				,       self._pytorch_dump_output				,       _UpperCamelCase					)
			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(_UpperCamelCase					)

				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(_UpperCamelCase					)

				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]'''					)








import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





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







			'''simple docstring'''

			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCamelCase							) for s in shape]							)}.npy'''







			def 				snake_case__(     self :				Any							)      ->List[str]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				int=(4, 4, 6_4, 6_4)							,   _UpperCamelCase :				Optional[int]=False							)      ->Tuple:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return image







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Optional[int]="CompVis/stable-diffusion-v1-4"							)      ->Optional[Any]:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	'''bf16''' if fpaa else None

									snake_case_,    snake_case_   =	FlaxUNetaDConditionModel.from_pretrained(
									    _UpperCamelCase							,   subfolder='''unet'''							,   dtype=_UpperCamelCase							,   revision=_UpperCamelCase							)
									return model, params







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[Any]=0							,   _UpperCamelCase :				Tuple=(4, 7_7, 7_6_8)							,   _UpperCamelCase :				List[Any]=False							)      ->int:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return hidden_states







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   shape=(4, 4, 9_6, 9_6)							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   shape=(4, 7_7, 1_0_2_4)							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)