


import os

import numpy
import onnx







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      Union[str, Any]    )     ->		Dict:

       """simple docstring"""

       a_ :		Tuple							= a.name
       a_ :		int							= b.name

       a_ :		Any							= ''
       a_ :		int							= ''

       a_ :		Optional[int]							= a == b

       a_ :		str							= name_a
       a_ :		Tuple							= name_b

       return res







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      List[Any]  ,   __A			:      Optional[Any]    )     ->		Optional[int]:

       """simple docstring"""

       for i, input_name in enumerate(node_proto.input    ):
              if input_name == name:
                     node_proto.input.insert(__snake_case  ,   __snake_case    )
                     node_proto.input.pop(i + 1    )

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g  ,   __snake_case  ,   __snake_case    )
              _graph_replace_input_with(node_proto.attribute[1].g  ,   __snake_case  ,   __snake_case    )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g  ,   __snake_case  ,   __snake_case    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      List[Any]  ,   __A			:      Optional[int]    )     ->		Optional[int]:

       """simple docstring"""

       for n in graph_proto.node:
              _node_replace_input_with(__snake_case  ,   __snake_case  ,   __snake_case    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      Tuple  ,   __A			:      Optional[Any]    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							= list(model.graph.initializer    )
       a_ :		int							= list(model_without_ext.graph.initializer    )
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              a_ :		Optional[Any]							= inits[i].name
              a_ :		Tuple							= inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]    )

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph  ,   __snake_case  ,   __snake_case    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str    )     ->		List[Any]:

       """simple docstring"""

       a_ :		str							= os.path.dirname(__snake_case    )
       a_ :		Union[str, Any]							= os.path.basename(__snake_case    )

       a_ :		Dict							= onnx.load(os.path.join(__snake_case  ,   __snake_case    )    )

       a_ :		Optional[int]							= list(model.graph.initializer    )

       a_ :		Optional[Any]							= set()
       a_ :		Union[str, Any]							= {}
       a_ :		Optional[int]							= []

       a_ :		Tuple							= 0

       for i in range(len(__snake_case    )    ):
              if i in dup_set:
                     continue

              for j in range(i + 1  ,   len(__snake_case    )    ):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]  ,   inits[j]    ):
                            dup_set.add(__snake_case    )
                            dup_set.add(__snake_case    )

                            a_ :		str							= inits[j].data_type
                            a_ :		Optional[Any]							= numpy.prod(inits[j].dims    )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: '  ,   __snake_case    )
                            total_reduced_size += mem_size

                            a_ :		str							= inits[i].name
                            a_ :		Optional[Any]							= inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(__snake_case    )
                            else:
                                   a_ :		Union[str, Any]							= [name_j]
                            ind_to_replace.append((j, i)    )

       print('total reduced size: '  ,   total_reduced_size / 10_24 / 10_24 / 10_24  ,   'GB'    )

       a_ :		int							= sorted(__snake_case    )
       _remove_dup_initializers_from_model(__snake_case  ,   __snake_case  ,   __snake_case    )

       a_ :		Optional[Any]							= 'optimized_' + model_file_name
       a_ :		Union[str, Any]							= os.path.join(__snake_case  ,   __snake_case    )
       onnx.save(__snake_case  ,   __snake_case    )

       return new_model






import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[Any]:
             # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
             # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
             # while running an external program

             # python one-liner segments

             # this must be loaded before socket.socket is monkey-patched
             a_ :		str							=  '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

             a_ :		Any							=  '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

             a_ :		Dict							=  '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

             # Force fetching the files so that we can use the cache
             a_ :		Dict							=  'hf-internal-testing/tiny-random-bert'
             BertConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
             BertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
             BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
             pipeline(task='fill-mask'					,							model=SCREAMING_SNAKE_CASE__					)

             # baseline - just load from_pretrained with normal network
             a_ :		Union[str, Any]							=  [sys.executable, '-c', '\n'.join([load, run, mock]					)]

             # should succeed
             a_ :		List[str]							=  self.get_env()
             # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
             a_ :		Any							=  '1'
             a_ :		Optional[Any]							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:
             # python one-liner segments
             # this must be loaded before socket.socket is monkey-patched
             a_ :		Tuple							=  '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

             a_ :		str							=  '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

             a_ :		Any							=  '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

             # Force fetching the files so that we can use the cache
             a_ :		Tuple							=  'hf-internal-testing/tiny-random-bert'
             BertConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
             BertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
             BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
             pipeline(task='fill-mask'					,							model=SCREAMING_SNAKE_CASE__					)

             # baseline - just load from_pretrained with normal network
             a_ :		Optional[Any]							=  [sys.executable, '-c', '\n'.join([load, run, mock]					)]

             # should succeed
             a_ :		Dict							=  self.get_env()
             a_ :		Dict							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
             # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
             # while running an external program

             # python one-liner segments

             # this must be loaded before socket.socket is monkey-patched
             a_ :		Any							=  '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

             a_ :		Dict							=  '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

             a_ :		Any							=  '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

             # baseline - just load from_pretrained with normal network
             a_ :		Any							=  [sys.executable, '-c', '\n'.join([load, run]					)]

             # should succeed
             a_ :		Tuple							=  self.get_env()
             a_ :		int							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)

             # next emulate no network
             a_ :		str							=  [sys.executable, '-c', '\n'.join([load, mock, run]					)]

             # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
             # env["TRANSFORMERS_OFFLINE"] = "0"
             # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
             # self.assertEqual(result.returncode, 1, result.stderr)

             # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
             a_ :		Dict							=  '1'
             a_ :		int							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Union[str, Any]:
             a_ :		Union[str, Any]							=  '\nfrom transformers import pipeline\n        '
             a_ :		Optional[Any]							=  '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

             a_ :		Any							=  '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
             a_ :		Union[str, Any]							=  self.get_env()
             a_ :		Optional[Any]							=  '1'
             a_ :		int							=  [sys.executable, '-c', '\n'.join([load, mock, run]					)]
             a_ :		Union[str, Any]							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							1					,							result.stderr					)
             self.assertIn(
                 'You cannot infer task automatically within `pipeline` when using offline mode'					,							result.stderr.decode().replace('\n'					,							''					)					,							)

      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Dict:
             a_ :		Optional[int]							=  '\nfrom transformers import AutoModel\n        '
             a_ :		Dict							=  '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

             # baseline - just load from_pretrained with normal network
             a_ :		Tuple							=  [sys.executable, '-c', '\n'.join([load, run]					)]

             # should succeed
             a_ :		Optional[Any]							=  self.get_env()
             a_ :		Dict							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)

             # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
             a_ :		Optional[int]							=  '1'
             a_ :		Optional[Any]							=  subprocess.run(SCREAMING_SNAKE_CASE__					,							env=SCREAMING_SNAKE_CASE__					,							check=SCREAMING_SNAKE_CASE__					,							capture_output=SCREAMING_SNAKE_CASE__					)
             self.assertEqual(result.returncode					,							0					,							result.stderr					)
             self.assertIn('success'					,							result.stdout.decode()					)


