import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow


class   lowercase      :




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any )   ->			Any:



												'''simple docstring'''

												return None



class   lowercase      :




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												return None



class   lowercase      (       unittest.TestCase       ):
							lowercase__  :    Union[str, Any]									=							[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]




							@require_tf
							@slow
							def 		__snake_case(  self				:   Optional[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(_UpperCamelCase		,							"tf"		,							12		,							**_UpperCamelCase )




							@require_torch
							@slow
							def 		__snake_case(  self				:   List[str] )   ->			int:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(_UpperCamelCase		,							"pt"		,							12		,							**_UpperCamelCase )




							@require_torch
							@slow
							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[str]:



												'''simple docstring'''

												from transformers import BertModel

												SCREAMING_SNAKE_CASE           =    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
												with NamedTemporaryFile(mode="w+t" ) as vocab_file:
																	vocab_file.write("\n".join(_UpperCamelCase ) )
																	vocab_file.flush()
																	SCREAMING_SNAKE_CASE           =    BertTokenizerFast(vocab_file.name )

												with TemporaryDirectory() as bert_save_dir:
																	SCREAMING_SNAKE_CASE           =    BertModel(BertConfig(vocab_size=len(_UpperCamelCase ) ) )
																	model.save_pretrained(_UpperCamelCase )
																	self._test_export(_UpperCamelCase		,							"pt"		,							12		,							_UpperCamelCase )




							@require_tf
							@slow
							def 		__snake_case(  self				:   Any )   ->			Dict:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	SCREAMING_SNAKE_CASE           =    self._test_export(_UpperCamelCase		,							"tf"		,							12		,							**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    quantize(Path(_UpperCamelCase ) )

																	# Ensure the actual quantized model is not bigger than the original one
																	if quantized_path.stat().st_size >= Path(_UpperCamelCase ).stat().st_size:
																						self.fail("Quantized model is bigger than initial ONNX model" )




							@require_torch
							@slow
							def 		__snake_case(  self				:   Any )   ->			List[Any]:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	SCREAMING_SNAKE_CASE           =    self._test_export(_UpperCamelCase		,							"pt"		,							12		,							**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    quantize(_UpperCamelCase )

																	# Ensure the actual quantized model is not bigger than the original one
																	if quantized_path.stat().st_size >= Path(_UpperCamelCase ).stat().st_size:
																						self.fail("Quantized model is bigger than initial ONNX model" )




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																						SCREAMING_SNAKE_CASE           =    Path(_UpperCamelCase ).joinpath("model.onnx" )

																	# Remove folder if exists
																	if path.parent.exists():
																						path.parent.rmdir()

																	# Export
																	convert(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase )

																	return path
												except Exception as e:
																	self.fail(_UpperCamelCase )




							@require_torch
							@require_tokenizers
							@slow
							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												from transformers import BertModel

												SCREAMING_SNAKE_CASE           =    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
												SCREAMING_SNAKE_CASE           =    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
												self._test_infer_dynamic_axis(_UpperCamelCase		,							_UpperCamelCase		,							"pt" )




							@require_tf
							@require_tokenizers
							@slow
							def 		__snake_case(  self				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												from transformers import TFBertModel

												SCREAMING_SNAKE_CASE           =    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
												SCREAMING_SNAKE_CASE           =    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
												self._test_infer_dynamic_axis(_UpperCamelCase		,							_UpperCamelCase		,							"tf" )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FeatureExtractionPipeline(_UpperCamelCase		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    infer_shapes(_UpperCamelCase		,							_UpperCamelCase )

												# Assert all variables are present
												self.assertEqual(len(_UpperCamelCase )		,							len(_UpperCamelCase ) )
												self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
												self.assertSequenceEqual(variable_names[:3]		,							_UpperCamelCase )
												self.assertSequenceEqual(variable_names[3:]		,							_UpperCamelCase )

												# Assert inputs are {0: batch, 1: sequence}
												for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																	self.assertDictEqual(shapes[var_name]		,							{0: "batch", 1: "sequence"} )

												# Assert outputs are {0: batch, 1: sequence} and {0: batch}
												self.assertDictEqual(shapes["output_0"]		,							{0: "batch", 1: "sequence"} )
												self.assertDictEqual(shapes["output_1"]		,							{0: "batch"} )




							def 		__snake_case(  self				:   Optional[int] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ["input_ids", "attention_mask", "token_type_ids"]
												SCREAMING_SNAKE_CASE           =    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    ensure_valid_input(FuncContiguousArgs()		,							_UpperCamelCase		,							_UpperCamelCase )

												# Should have exactly the same number of args (all are valid)
												self.assertEqual(len(_UpperCamelCase )		,							3 )

												# Should have exactly the same input names
												self.assertEqual(set(_UpperCamelCase )		,							set(_UpperCamelCase ) )

												# Parameter should be reordered according to their respective place in the function:
												# (input_ids, token_type_ids, attention_mask)
												self.assertEqual(_UpperCamelCase		,							(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"]) )

												# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    ensure_valid_input(FuncNonContiguousArgs()		,							_UpperCamelCase		,							_UpperCamelCase )

												# Should have exactly the one arg (all before the one not provided "some_other_args")
												self.assertEqual(len(_UpperCamelCase )		,							1 )
												self.assertEqual(len(_UpperCamelCase )		,							1 )

												# Should have only "input_ids"
												self.assertEqual(inputs_args[0]		,							tokens["input_ids"] )
												self.assertEqual(ordered_input_names[0]		,							"input_ids" )





							def 		__snake_case(  self				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    generate_identified_filename(Path("/home/something/my_fake_model.onnx" )		,							"-test" )
												self.assertEqual("/home/something/my_fake_model-test.onnx"		,							generated.as_posix() )





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					try:
										with open(UpperCAmelCase__ ,	"rb"					) as flax_state_f:
															SCREAMING_SNAKE_CASE           =    from_bytes(UpperCAmelCase__ ,	flax_state_f.read()					)
					except UnpicklingError as e:
										try:
															with open(UpperCAmelCase__					) as f:
																				if f.read().startswith("version"					):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."					)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "					)

					return load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
										    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
										    " instructions."					)
										raise

					# check if we have bf16 weights
					SCREAMING_SNAKE_CASE           =    flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__					: x.dtype == jnp.bfloataa ,	UpperCAmelCase__					)					).values()
					if any(UpperCAmelCase__					):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
										    "before loading those in PyTorch model."					)
										SCREAMING_SNAKE_CASE           =    jax.tree_util.tree_map(
										    lambda UpperCAmelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    flatten_dict(UpperCAmelCase__ ,	sep="."					)
					SCREAMING_SNAKE_CASE           =    pt_model.state_dict()

					# keep track of unexpected & missing keys
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    set(pt_model_dict.keys()					)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										SCREAMING_SNAKE_CASE           =    flax_key_tuple.split("."					)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    jnp.transpose(UpperCAmelCase__ ,	(3, 2, 0, 1)					)
										elif flax_key_tuple_array[-1] == "kernel":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    (
																				    flax_key_tuple_string.replace("_0" ,	".0"					)
																				    .replace("_1" ,	".1"					)
																				    .replace("_2" ,	".2"					)
																				    .replace("_3" ,	".3"					)
																				    .replace("_4" ,	".4"					)
																				    .replace("_5" ,	".5"					)
																				    .replace("_6" ,	".6"					)
																				    .replace("_7" ,	".7"					)
																				    .replace("_8" ,	".8"					)
																				    .replace("_9" ,	".9"					)
																				)

										SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																				    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."					)
															else:
																				# add weight to pytorch dict
																				SCREAMING_SNAKE_CASE           =    np.asarray(UpperCAmelCase__					) if not isinstance(UpperCAmelCase__ ,	np.ndarray					) else flax_tensor
																				SCREAMING_SNAKE_CASE           =    torch.from_numpy(UpperCAmelCase__					)
																				# remove from missing keys
																				missing_keys.remove(UpperCAmelCase__					)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(UpperCAmelCase__					)

					pt_model.load_state_dict(UpperCAmelCase__					)

					# re-transform missing_keys to list
					SCREAMING_SNAKE_CASE           =    list(UpperCAmelCase__					)

					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    "Some weights of the Flax model were not used when initializing the PyTorch model"
										    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
										    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
										    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
										    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
										    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
										    " FlaxBertForSequenceClassification model)."					)
					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
										    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
										    " use it for predictions and inference."					)

					return pt_model




